October 25th, 2009 | 
Author: IT consultant employed with Rockaway Technology.
www.rtginc.net
Posts Tagged ‘Network’
Changing Scenario of Information Exchange by Network Administration
Network Administration is different from System Administration. In System Administration all tasks are concerned with one independent computer system. In case of Network Administration once you put your system on a network, it interacts with many other systems. In today’s fast paced world everybody is demanding for modern network which is much superior to the earlier ones. We need improved security and better network management.
Network administration commonly includes activities such as network address assignment, routing protocols and configuration of Authentication and Authorization –directory services. It often includes maintenance of network facilities in individual machines, such as drivers and settings of personal computers. It sometimes also includes maintenance of certain network: gateways, intrusion detection system, etc.
The task of Network Administration is done by Network Administrator. Network Administrator is responsible for network design and security.
Designing:
Designing a network is termed as Network Architecture. Network Architecture can be defined as the design principles, physical configuration, functional organization, procedures, and data structures used for designing and construction of a communication network.
In this fast changing scenario the term Network Architecture also denote classification and differentiation of distributed computing architecture.
To tackle with the collision issue a certain set of rules are also defined which ensures not more than one computer can send across a packet through data cable.
Security:
Sometimes security has more to do with politics and human resources issue than networking. A security administrator is mostly put into abeyance in deciding priority to maintain a reasonable level of security or providing flexibility to users to get their job done. A security administrator has to seek balance between these two opposite wants. Security should be like clothing as clothing are so designed that they are not to tight so that it does not restrict movement and it should not be so loose that it gets revealed to public.
When thinking of securing a corporate network three ways should be kept in mind that someone can get access to the corporate network:
1) Through the Internet.
2) Through dial-in-access.
3) Through Virtual Private Networks.
Network Management System:
Network Management Systems is a combination of both the hardware and software used to monitor and administer a network. Individual network elements are monitored by Element Management Systems.
Network management refers to the activities, methods, procedures, and tools that pertain to the Operation, administration, maintenance, and provisioning of networked systems.
• Operation deals with keeping the network (and the services that the network provides) up and running smoothly. It includes monitoring the network to spot problems as soon as possible, ideally before users are affected.
• Administration deals with keeping track of resources in the network and how they are assigned. It includes all the “housekeeping” that is necessary to keep the network under control.
• Maintenance is concerned with performing repairs and upgrades – for example, when equipment must be replaced, when a router needs a patch for an operating system image, when a new switch is added to a network. Maintenance also involves corrective and preventive measures to make the managed network run “better”, such as adjusting device configuration parameters.
• Provisioning is concerned with configuring resources in the network to support a given service. For example, this might include setting up the network so that a new customer can receive voice service.
Changing Dynamics of Network Administration:
Perhaps you prefer to work outside the office, say, on the golf course. When you’re away it would be useful to monitor your servers and network remotely. You’re in luck; there are a bevy of utilities that let you monitor, diagnose, and repair problems and perform administration tasks remotely with a smartphone or handheld device linked to one of your data center servers.
Most offerings let you manage passwords, printer connections, email programs, or database servers with a Blackberry, Palm Tree, or Nokia smartphone linked to a network server, which is linked to the rest of your network. But while Avocent’s Sonic-Admin, Ecutel’s IC2 (Infrastructure Command and Control), MobileControl from ASG, and other solutions probably work well; many administrations have reservations about security.
Security Concerns
The idea of network management applications using a wireless device represents a dream come true for many network administrators, but it will be a while before the concept takes hold. In fact, the potential market for such utilities is huge, but security concerns have reduced it to only a fledgling market, and the utility’s debut has been too small to track, says analyst Gerry Purdy of Mobile Trax.
“Of course, demand for this type of application will eventually grow as smartphones will have more feature sets and can handle the software loads necessary,” Purdy says. “But at the same time, these devices and applications represent security [threats] in the minds of [IT administrators], such as when a person leaves the company and has access to the network with his mobile device or when a mobile device might provide the opportunity for someone to hack your network with a smartphone.”
Acknowledging that appeasing security concerns represents the toughest sell, Robert Touw, a business development manager for Avocent’s mobile solutions group, maintains that IT administrators are increasingly buying into both the idea of remote network management and the security that the utilities can offer. He also says that even some financial firms, for which security is of utmost concern, now use the utility.
Wireless & Remote Freedom
Most of all, IT administrators relish the opportunities wireless and remote network administration offers. “There is quality of life: Suddenly you don’t have to give up dinner to fix something right away,” Touw says. “Now, suddenly you can also do things in 10 minutes that used to take 40 minutes.”
For security, Avocent says SonicAdmin offers 3DES encryption and token transaction authentication so that the server only accepts valid data packets with recognized tokens. Authentication levels, backed by RSA SecurID, include SonicAdmin user account authentication, device authentication, and NT/AD authentication plus optional RSA SecurID integration. Data such as confidential network and personal logon is not stored on mobile devices. Additionally, remote administrative actions are logged on both the SonicAdmin server and managed servers.
Ecutel says its IC2 software offers six security layers. The server, on which it runs, for example, sits behind the data center’s firewall and signals to and from the server, and mobile devices use a minimum of 128-bit encryption. Authentication is based on a Web-based administration client and RSA SecurID. There is also a complete audit trail of IC2 server operation, and IC2 automatically times out after 30 minutes of inactivity.
ASG says users of its MobileControl Administrator’s management interface are required to log in using a PIN and domain credentials. The systems also can take advantage of RSA SecurID, SSL, VPN, and third-party wireless gateway server security solutions. Sensitive information, such as passwords, is never stored on the wireless device, and an automatic timeout feature for each mobile device prevents illegal entry into the network if a wireless device is lost or stolen.
According to Purdy, the application will likely eventually take hold once vendors convince IT administrators that the security risks can be minimized. Purdy says, “It is just a matter of these companies getting the word out that [these utilities] are secure and that they work.”
Posted in Networking | 
Tags: Administration, Changing, Exchange, information, Network, Scenario | 
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Understandings Network Management and Network Monitoring
Network management may mean different things to different people. To some network management may be a solitary network consultant monitoring network activity with an outdated protocol analyzer, to others network management may be about distributed database, high-end workstations generating and traffic. Speaking generally, network management is a service, which uses a wide range of devices, tools, and applications, to enable the network managers to monitor and maintain networks successfully & efficiently.
Network management deals with the top-level administration and maintenance of widespread and large networks, commonly seen in the field of computers or telecommunications, which may be necessarily, include user terminal equipment.
Network management executes functions such as security, control, allocation, monitoring, coordination, deployment and planning to name a few. It is also worth noting that network management is governed by a several protocols which are basically present there for its support, including SNMP, Common Information Model, CMIP, WBEM, Transaction Language 1, Java Management Extensions, and Netconf.
Routing is also an important area of network management. Routing refers to the process of selecting the paths in a computer network on which to send data. In this arena of network management, logically addressed packets get transported from their source to their destination with the help of nodes. These nodes are called routers, in a process termed as forwarding.
Successful network management also uses accounting management. This controls and reports on the financial status of the network. This area of network management involves bank account maintenance, financial statement development, and analysis of cash flow and financial health.
Coming to Network Monitoring, it is about policing network traffic. In other words, network monitoring is spying for the benefit of smooth working of network management. Network monitoring is part of network management. Ideally network monitoring is a function that one of your systems must perform on an ongoing basis. While the other systems are performing the functions assigned to them, one should set aside at least one computer to monitor network activity. This is network monitoring in a nutshell.
The computer performing network monitoring must be kept always on. Which means that network monitoring system should have exclusive power lines or, backup generator facility. Everyone should understand that network-monitoring system is the most critical part of any network, because it is with the help of network monitoring that that the alarm will be sent if something is wrong.
Network monitoring will identify the slow or failing systems and notify the network administrator of such lapses. Issues like overloaded systems, crashing of servers, network connections being lost, virus infections, and power outages will be dealt without losing time if network monitoring is in place.
For more resources about Network management or even about Network monitoring please review this site http://www.networkstrategy.com
Social Networking for Network Marketers: the New Era
Can you guess what the big buzz is lately for network marketers? Social Networking! I will explain why but let’s take a quick look at how it started. At first there was MySpace and Facebook, two huge social networking giants. Network Marketers have joined them in order to market their opportunities through these sites. It is a great way for that extra free exposure even though the members of the networks aren’t entirely targeted.
However, the value of marketing on Myspace and Facebook is quickly dropping. This is due to the massive release of new and niche focused social networks. The fact is that this year alone I have seen an increasing number of professional social networks geared towards network marketers launching and becoming increasingly popular. To name a few: TalkMoola.com, Zenzuu, Friendswin, Yuwie, and Wowzza. A couple older ones are DirectMatches, AdLandPro, and even Ryze. Honestly I wouldn’t even be the least bit surprised to see a few more start-up this year – that is how much it is catching on! It is also obvious why they do become so popular among network marketers, because there is no better advertising than targeted advertising. Exposing your business opportunity, system, or product to other network marketers is simply the most effective thing to do. Once a network marketer joins you they already have had their foot in the door and are somewhat knowledgeable about the industry and can greatly help your business. However if a random person from MySpace sees and joins your business they are not nearly as valuable to you because they most likely have no idea what they are doing or what network marketing is even about.
So when network marketers are invited with the ability to join a social network full of network marketers it is almost a no-brainer. Just to clarify why I said “almost”… It is because not all network marketers have caught on to the power of social networking yet or how to properly use social networking to explode their businesses. But the ability to further expose themselves and their business to other like-minded people is extremely valuable and profitable for most. The old way of network marketing is out, and I see the future holding tight to social marketing through professional social networks. Even so, many of these new business social networks are offering incentives for those who join. These range from revenue sharing, direct affiliate commissions, and various MLM pay plans. Through this supporting these new business social networks they will experience the increased awareness and exposure that they deserve.
All professional social networks, however, are not created equal. Here are a few things you need to look out for when deciding which ones you will utilize in exposing yourself and your opportunities.
1) Somewhat Targeted or Extremely Targeted?
When looking over your social networks find out if the people involved in the social network are the type of people who would be interested in what you have to offer, or are like-minded to you. This means that if you are a network marketer looking to provide an opportunity to other network marketers then the people involved in the social network should be strictly network marketers. Does the social network you are considering include members looking for a date or love? If the social network includes this or niches totally unrelated to you then this weakens the potential of that network for you. However, if the social network is only network marketers or business opportunity seekers then it is an incredible source for you.
2) Are there limitations on contacting your friends in bulk?
Increasing your friends or contact list on social networks is the one major thing you must do in order to maximize your exposure and reach. This means accepting friend invitations from those within the social network and also seeking out other like-minded to add to your network of friends. The larger your list of friends the more active you will appear to be and the more people will take heart to what you have to say. It also means more people who you can send messages to through the social network. Some social networks limit the amount of people you are able to send a message to at once. Let’s say you have 200 friends but you are limited to sending to only 30 people per message and you can only send one bulk message per day. Well this is a very negative restriction and even more so if you have a much larger network of friends. On the other hand, some social networks will let you add unlimited friends and you can send them a bulletin or message to all your friends at once anytime with ease. This is what you should be looking for because whether you have 100 friends, 1,000 or even 10,000 you can send them all a message just as easily as if you were sending it to one. This means potentially huge exposure for you. If you want you can even consider it like an instant contact list of targeted prospects! However, I do warn about sending too much unwanted messages to your friends, you should try to connect with them and send them as much quality information as you can instead and then lightly introduce what you have to offer. If you consistently send what they would consider as spam messages they will just block you or remove you from their friends’ network, so using the social network properly is important.
3) Are the members free, paid, or both?
Another factor is the true quality of the member base. If the membership is mostly free then they are less likely to be purchase ready. However, if the membership is all paid and everyone is used to paying to be a part of the social network then these are the highest quality members you can get in touch with because they will have the money to invest in working with you. Mixed is fine though as this factor is not as important as the first two.
4) How are the other site incentives?
This is the least important but should still be mentioned. Other incentives could be the pay plan such as revenue sharing, affiliate commissions, or some type of matrix structure. It could also be other services provided with your cost of the membership such as training, tools, or other bonuses. Keep in mind, even if they do not offer great incentives but the rest of the above factors are positive I would still be excited to join.
Right now there is not a huge selection of targeted professional social networks but I am positive that the future will hold true for more and more becoming available. Personally I like to take advantage and join any social network because that is extra exposure for me, and any extra exposure is always a good thing. However when choosing to be most active in select networks you may want to consider the above factors. I have created many strong relationships, learned a lot, taught a lot, and profited a lot from those relationships within social networks. You should take at least some advatange of every professional social network available, even if it is just setting up a profile to gain some extra exposure. If they are a niche social network that have like-minded inviduals similar to yourself then you should definitely be more open to actively using that network. The benefits can be enormous.
Darren Olander is dedicated to teaching others how to create a success online through internet network marketing strategies. He is a site owner, article writer, coach & marketing consultant enjoying the benefits of working full time from home. Learn more about him at http://www.darrenolander.com
Mybuxnetworks, Great Ptc Network for Newbie Making Money Online
Many online money maker think about Paid to click (PTC) is easiest way to make money, because of some reasons:
+ Everyone can do it : no skills require, all the things you need, that’s have a computer with internet access
+ No cost (fee) : signing up on PTC is completely free, you don’t have to pay any money when become a member
+ PTC is starting place for learning : PTC sites is good for the newbies who are still learning money making online. You could spend about 15 minutes each day, also you can do something else when you click advertising on these sites.
+ Only thing you need : just click advertise everyday, and be patient until you got paid, if you can not be patient, you can not make money with PTC
PTC sites are joined by thousands of users everyday and it’s a very popular way of making money. Sadly, there are also many sites which are scam and new users with less or no experience easily get into their trap. I’ll list some helpful tips here to avoid such scams.
+ Join make money online forums: check the good sites really pay, in the forums, PTC sites often posted in Get paid to programs section. Most of PTC sites in this is trusted, and have many members signed up.
+ Watching on PTC monitors, too see which sites is paying, which is not paying, or problem : you should join sites are paying, so you have chances to get money, do not join the sites are problem, or still not pay.
+ Besides these forums, you can find many blogs and websites maintained by individuals who are in this business for a long time. Always try to sign up through the referral link of some person or if you are interested in some site (you can use search engine to find some PTC sites, always include payment proof keyword, such as : paid to click with payment proof). You can joined under refs link of someone else in forums, blogs, websites about PTC, you lose nothing when do that, morever you can help your friend make more money, and mostly people checked these sites before they joined.
Many PTCs exist on the Internet in short time, so seem difficult to earn money from this kind of business, the better way : you should join the sites which payment are instant, you will not have wait days, months to get paid, or you can join PTC sites are really trust (look on forums of these PTC sites to know about admin of sites, he can be trust or not)
I checked many PTC sites, and I feel happy when joined MyBuxNetworks (I found admin here very trust and honest), the PTC network include 4 separate sites: EarnMyBux, MakeMyBux, ClickMyBux, CashMyBux, this is a great opputurnity to earn, you can earn money as many times as others site, and the most important thing you should join them because they stay long (insprite many other PTC gone so fast).
In each site of MyBuxNetworks, have a lot of advertise for you click every (they have many advertisers buy advertising packet from them), you can get money for sure. You have been paid when reach minimum payout 10$, payment made via AlertPay or PayPal, you can buy referals to increase your earning (if you feel really believe : you can take a look on their forum to make purchase decisions, I saw many people buy referals and they post their purchae proofs)
If you are still not member of MyBuxNetworks, and you like to make money from paid to click industry, join each site of MyBux and start earning.
EarnMyBux
MakeMyBux
ClickMyBux
CashMyBux
You simply click ads everyday, and wait your payment after you make a cashout request, many ads for you click everyday.
Minimum ads daily = 15
Free member:
Per click = $0.005
Per referral click = $0.0025
Premium (upgraded member):
Per click = $0.01
Per referral click = $0.005
Minimum payout = $10
The great thing about MyBuxNetworks, they host on dedicated server, so their site is fast access, and can be safe from Ddos attack, and many kinds of attackers. Become a member so easy, and absolutely free, please sign up:
1. EarnMyBux
2. MakeMyBux
3. ClickMyBux
I’m sure you can not find any PTC sites better than MyBuxNetworks.
Wish everyone can earn much money here!
Posted in Networking |
Tags: Great, Making, Money, Mybuxnetworks, Network, Newbie, Online |
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neural network
What is a Neural Network?
First of all, when we are talking about a neural network, we should more properly say “artificial neural network” (ANN), because that is what we mean most of the time. Biological neural networks are much more complicated than the mathematical models we use for ANNs. But it is customary to be lazy and drop the “A” or the “artificial”.
An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well.
Historical Background of Neural Networks
Neural network simulations appear to be a recent development. However, this field was established before the advent of computers, and has survived at least one major setback and several eras.
Many importand advances have been boosted by the use of inexpensive computer emulations. Following an initial period of enthusiasm, the field survived a period of frustration and disrepute. During this period when funding and professional support was minimal, important advances were made by relatively few reserchers. These pioneers were able to develop convincing technology which surpassed the limitations identified by Minsky and Papert. Minsky and Papert, published a book (in 1969) in which they summed up a general feeling of frustration (against neural networks) among researchers, and was thus accepted by most without further analysis. Currently, the neural network field enjoys a resurgence of interest and a corresponding increase in funding.
The history of neural networks that was described above can be divided into several periods:
First Attempts: There were some initial simulations using formal logic. McCulloch and Pitts (1943) developed models of neural networks based on their understanding of neurology. These models made several assumptions about how neurons worked. Their networks were based on simple neurons which were considered to be binary devices with fixed thresholds. The results of their model were simple logic functions such as “a or b” and “a and b”. Another attempt was by using computer simulations. Two groups (Farley and Clark, 1954; Rochester, Holland, Haibit and Duda, 1956). The first group (IBM reserchers) maintained closed contact with neuroscientists at McGill University. So whenever their models did not work, they consulted the neuroscientists. This interaction established a multidiscilinary trend which continues to the present day.
Promising & Emerging Technology: Not only was neroscience influential in the development of neural networks, but psychologists and engineers also contributed to the progress of neural network simulations. Rosenblatt (1958) stirred considerable interest and activity in the field when he designed and developed the Perceptron. The Perceptron had three layers with the middle layer known as the association layer.This system could learn to connect or associate a given input to a random output unit.
Another system was the ADALINE (ADAptive LInear Element) which was developed in 1960 by Widrow and Hoff (of Stanford University). The ADALINE was an analogue electronic device made from simple components. The method used for learning was different to that of the Perceptron, it employed the Least-Mean-Squares (LMS) learning rule.
Period of Frustration & Disrepute: In 1969 Minsky and Papert wrote a book in which they generalised the limitations of single layer Perceptrons to multilayered systems. In the book they said: “…our intuitive judgment that the extension (to multilayer systems) is sterile”. The significant result of their book was to eliminate funding for research with neural network simulations. The conclusions supported the disenhantment of reserchers in the field. As a result, considerable prejudice against this field was activated.
Innovation: Although public interest and available funding were minimal, several researchers continued working to develop neuromorphically based computaional methods for problems such as pattern recognition.
During this period several paradigms were generated which modern work continues to enhance.Grossberg’s (Steve Grossberg and Gail Carpenter in 1988) influence founded a school of thought which explores resonating algorithms. They developed the ART (Adaptive Resonance Theory) networks based on biologically plausible models. Anderson and Kohonen developed associative techniques independent of each other. Klopf (A. Henry Klopf) in 1972, developed a basis for learning in artificial neurons based on a biological principle for neuronal learning called heterostasis.
Werbos (Paul Werbos 1974) developed and used the back-propagation learning method, however several years passed before this approach was popularized. Back-propagation nets are probably the most well known and widely applied of the neural networks today. In essence, the back-propagation net. is a Perceptron with multiple layers, a different thershold function in the artificial neuron, and a more robust and capable learning rule.
Amari (A. Shun-Ichi 1967) was involved with theoretical developments: he published a paper which established a mathematical theory for a learning basis (error-correction method) dealing with adaptive patern classification. While Fukushima (F. Kunihiko) developed a step wise trained multilayered neural network for interpretation of handwritten characters. The original network was published in 1975 and was called the Cognitron.
Re-Emergence: Progress during the late 1970s and early 1980s was important to the re-emergence on interest in the neural network field. Several factors influenced this movement. For example, comprehensive books and conferences provided a forum for people in diverse fields with specialized technical languages, and the response to conferences and publications was quite positive. The news media picked up on the increased activity and tutorials helped disseminate the technology. Academic programs appeared and courses were inroduced at most major Universities (in US and Europe). Attention is now focused on funding levels throughout Europe, Japan and the US and as this funding becomes available, several new commercial with applications in industry and finacial institutions are emerging.
Today: Significant progress has been made in the field of neural networks-enough to attract a great deal of attention and fund further research. Advancement beyond current commercial applications appears to be possible, and research is advancing the field on many fronts. Neurally based chips are emerging and applications to complex problems developing. Clearly, today is a period of transition for neural network technology.
Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an “expert” in the category of information it has been given to analyse. This expert can then be used to provide projections given new situations of interest and answer “what if” questions.
Other advantages include:
- Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.
- Self-Organisation: An ANN can create its own organisation or representation of the information it receives during learning time.
- Real Time Operation: ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.
- Fault Tolerance via Redundant Information Coding: Partial destruction of a network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage.
Neural networks versus conventional computers
Neural networks take a different approach to problem solving than that of conventional computers. Conventional computers use an algorithmic approach i.e. the computer follows a set of instructions in order to solve a problem. Unless the specific steps that the computer needs to follow are known the computer cannot solve the problem. That restricts the problem solving capability of conventional computers to problems that we already understand and know how to solve. But computers would be so much more useful if they could do things that we don’t exactly know how to do.
Neural networks process information in a similar way the human brain does. The network is composed of a large number of highly interconnected processing elements(neurones) working in parallel to solve a specific problem. Neural networks learn by example. They cannot be programmed to perform a specific task. The examples must be selected carefully otherwise useful time is wasted or even worse the network might be functioning incorrectly. The disadvantage is that because the network finds out how to solve the problem by itself, its operation can be unpredictable.
On the other hand, conventional computers use a cognitive approach to problem solving; the way the problem is to solved must be known and stated in small unambiguous instructions. These instructions are then converted to a high level language program and then into machine code that the computer can understand. These machines are totally predictable; if anything goes wrong is due to a software or hardware fault.
Neural networks and conventional algorithmic computers are not in competition but complement each other. There are tasks are more suited to an algorithmic approach like arithmetic operations and tasks that are more suited to neural networks. Even more, a large number of tasks, require systems that use a combination of the two approaches (normally a conventional computer is used to supervise the neural network) in order to perform at maximum efficiency.
Neural Networks in Practice
Given this description of neural networks and how they work, what real world applications are they suited for? Neural networks have broad applicability to real world business problems. In fact, they have already been successfully applied in many industries.
Since neural networks are best at identifying patterns or trends in data, they are well suited for prediction or forecasting needs including:
sales forecasting
industrial process control
customer research
data validation
risk management
target marketing
But to give you some more specific examples; ANN are also used in the following specific paradigms: recognition of speakers in communications; diagnosis of hepatitis; recovery of telecommunications from faulty software; interpretation of multimeaning Chinese words; undersea mine detection; texture analysis; three-dimensional object recognition; handwritten word recognition; and facial recognition.
Human and Artificial Neurones – investigating the similarities
How the Human Brain Learns?
Much is still unknown about how the brain trains itself to process information, so theories abound. In the human brain, a typical neuron collects signals from others through a host of fine structures called dendrites. The neuron sends out spikes of electrical activity through a long, thin stand known as an axon, which splits into thousands of branches. At the end of each branch, a structure called a synapse converts the activity from the axon into electrical effects that inhibit or excite activity from the axon into electrical effects that inhibit or excite activity in the connected neurones. When a neuron receives excitatory input that is sufficiently large compared with its inhibitory input, it sends a spike of electrical activity down its axon. Learning occurs by changing the effectiveness of the synapses so that the influence of one neuron on another changes.
Components of a neuron
The synapse
From Human Neurones to Artificial Neurones
We conduct these neural networks by first trying to deduce the essential features of neurones and their interconnections. We then typically program a computer to simulate these features. However because our knowledge of neurones is incomplete and our computing power is limited, our models are necessarily gross idealisations of real networks of neurones.
The neuron model
Architecture of neural networks
Feed-forward networks
Feed-forward ANNs allow signals to travel one way only; from input to output. There is no feedback (loops) i.e. the output of any layer does not affect that same layer. Feed-forward ANNs tend to be straight forward networks that associate inputs with outputs. They are extensively used in pattern recognition. This type of organisation is also referred to as bottom-up or top-down.
Feedback networks
Feedback networks (figure 1) can have signals travelling in both directions by introducing loops in the network. Feedback networks are very powerful and can get extremely complicated. Feedback networks are dynamic; their ‘state’ is changing continuously until they reach an equilibrium point. They remain at the equilibrium point until the input changes and a new equilibrium needs to be found. Feedback architectures are also referred to as interactive or recurrent, although the latter term is often used to denote feedback connections in single-layer organisations.
Applications of neural networks
Neural Networks in Practice
Given this description of neural networks and how they work, what real world applications are they suited for? Neural networks have broad applicability to real world business problems. In fact, they have already been successfully applied in many industries.
Since neural networks are best at identifying patterns or trends in data, they are well suited for prediction or forecasting needs including:
sales forecasting
industrial process control
customer research
data validation
risk management
target marketing
But to give you some more specific examples; ANN are also used in the following specific paradigms: recognition of speakers in communications; diagnosis of hepatitis; recovery of telecommunications from faulty software; interpretation of multimeaning Chinese words; undersea mine detection; texture analysis; three-dimensional object recognition; hand-written word recognition; and facial recognition.
Neural networks in medicine
Artificial Neural Networks (ANN) are currently a ‘hot’ research area in medicine and it is believed that they will receive extensive application to biomedical systems in the next few years. At the moment, the research is mostly on modelling parts of the human body and recognising diseases from various scans (e.g. cardiograms, CAT scans, ultrasonic scans, etc.).
Neural networks are ideal in recognising diseases using scans since there is no need to provide a specific algorithm on how to identify the disease. Neural networks learn by example so the details of how to recognise the disease are not needed. What is needed is a set of examples that are representative of all the variations of the disease. The quantity of examples is not as important as the ‘quantity’. The examples need to be selected very carefully if the system is to perform reliably and efficiently.
Modelling and Diagnosing the Cardiovascular System
Neural Networks are used experimentally to model the human cardiovascular system. Diagnosis can be achieved by building a model of the cardiovascular system of an individual and comparing it with the real time physiological measurements taken from the patient. If this routine is carried out regularly, potential harmful medical conditions can be detected at an early stage and thus make the process of combating the disease much easier.
A model of an individual’s cardiovascular system must mimic the relationship among physiological variables (i.e., heart rate, systolic and diastolic blood pressures, and breathing rate) at different physical activity levels. If a model is adapted to an individual, then it becomes a model of the physical condition of that individual. The simulator will have to be able to adapt to the features of any individual without the supervision of an expert. This calls for a neural network.
Another reason that justifies the use of ANN technology, is the ability of ANNs to provide sensor fusion which is the combining of values from several different sensors. Sensor fusion enables the ANNs to learn complex relationships among the individual sensor values, which would otherwise be lost if the values were individually analysed. In medical modelling and diagnosis, this implies that even though each sensor in a set may be sensitive only to a specific physiological variable, ANNs are capable of detecting complex medical conditions by fusing the data from the individual biomedical sensors.
ANNs are used experimentally to implement electronic noses. Electronic noses have several potential applications in telemedicine. Telemedicine is the practice of medicine over long distances via a communication link. The electronic nose would identify odours in the remote surgical environment. These identified odours would then be electronically transmitted to another site where an door generation system would recreate them. Because the sense of smell can be an important sense to the surgeon, telesmell would enhance telepresent surgery.
For more information on telemedicine and telepresent surgery
Electronic noses
ANNs are used experimentally to implement electronic noses. Electronic noses have several potential applications in telemedicine. Telemedicine is the practice of medicine over long distances via a communication link. The electronic nose would identify odours in the remote surgical environment. These identified odours would then be electronically transmitted to another site where an door generation system would recreate them. Because the sense of smell can be an important sense to the surgeon, telesmell would enhance telepresent surgery.
For more information on telemedicine and telepresent surgery
Neural Networks in business
Business is a diverted field with several general areas of specialisation such as accounting or financial analysis. Almost any neural network application would fit into one business area or financial analysis.
There is some potential for using neural networks for business purposes, including resource allocation and scheduling. There is also a strong potential for using neural networks for database mining, that is, searching for patterns implicit within the explicitly stored information in databases. Most of the funded work in this area is classified as proprietary. Thus, it is not possible to report on the full extent of the work going on. Most work is applying neural networks, such as the Hopfield-Tank network for optimization and scheduling.
There is a marketing application which has been integrated with a neural network system. The Airline Marketing Tactician (a trademark abbreviated as AMT) is a computer system made of various intelligent technologies including expert systems. A feedforward neural network is integrated with the AMT and was trained using back-propagation to assist the marketing control of airline seat allocations. The adaptive neural approach was amenable to rule expression. Additionaly, the application’s environment changed rapidly and constantly, which required a continuously adaptive solution. The system is used to monitor and recommend booking advice for each departure. Such information has a direct impact on the profitability of an airline and can provide a technological advantage for users of the system. [Hutchison & Stephens, 1987]
While it is significant that neural networks have been applied to this problem, it is also important to see that this intelligent technology can be integrated with expert systems and other approaches to make a functional system. Neural networks were used to discover the influence of undefined interactions by the various variables. While these interactions were not defined, they were used by the neural system to develop useful conclusions. It is also noteworthy to see that neural networks can influence the bottom line.
Are there any limits to Neural Networks?
The major issues of concern today are the scalability problem, testing, verification, and integration of neural network systems into the modern environment. Neural network programs sometimes become unstable when applied to larger problems. The defence, nuclear and space industries are concerned about the issue of testing and verification. The mathematical theories used to guarantee the performance of an applied neural network are still under development. The solution for the time being may be to train and test these intelligent systems much as we do for humans. Also there are some more practical problems like:
- the operational problem encountered when attempting to simulate the parallelism of neural networks. Since the majority of neural networks are simulated on sequential machines, giving rise to a very rapid increase in processing time requirements as size of the problem expands.
Solution: implement neural networks directly in hardware, but these need a lot of development still. - instability to explain any results that they obtain. Networks function as “black boxes” whose rules of operation are completely unknown
The Future
Because gazing into the future is somewhat like gazing into a crystal ball, so it is better to quote some “predictions”. Each prediction rests on some sort of evidence or established trend which, with extrapolation, clearly takes us into a new realm.
Prediction 1:
Neural Networks will fascinate user-specific systems for education, information processing, and entertainment. “Alternative ralities”, produced by comprehensive environments, are attractive in terms of their potential for systems control, education, and entertainment. This is not just a far-out research trend, but is something which is becoming an increasing part of our daily existence, as witnessed by the growing interest in comprehensive “entertainment centers” in each home.
This “programming” would require feedback from the user in order to be effective but simple and “passive” sensors (e.g fingertip sensors, gloves, or wristbands to sense pulse, blood pressure, skin ionisation, and so on), could provide effective feedback into a neural control system. This could be achieved, for example, with sensors that would detect pulse, blood pressure, skin ionisation, and other variables which the system could learn to correlate with a person’s response state.
Prediction 2:
Neural networks, integrated with other artificial intelligence technologies, methods for direct culture of nervous tissue, and other exotic technologies such as genetic engineering, will allow us to develop radical and exotic life-forms whether man, machine, or hybrid.
Prediction 3:
Neural networks will allow us to explore new realms of human capabillity realms previously available only with extensive training and personal discipline. So a specific state of consiously induced neurophysiologically observable awareness is necessary in order to facilitate a man machine system interface.
Conclusion
The computing world has a lot to gain fron neural networks. Their ability to learn by example makes them very flexible and powerful. Furthermore there is no need to devise an algorithm in order to perform a specific task; i.e. there is no need to understand the internal mechanisms of that task. They are also very well suited for real time systems because of their fast responseand computational times which are due to their parallel architecture.
Neural networks also contribute to other areas of research such as neurology and psychology. They are regularly used to model parts of living organisms and to investigate the internal mechanisms of the brain.
Perhaps the most exciting aspect of neural networks is the possibility that some day ‘consious’ networks might be produced. There is a number of scientists arguing that conciousness is a ‘mechanical’ property and that ‘consious’ neural networks are a realistic possibility.
Finally, I would like to state that even though neural networks have a huge potential we will only get the best of them when they are intergrated with computing, AI, fuzzy logic and related subjects.