Fuzzy neural network

A fuzzy neural network or neuro-fuzzy system is a learning machine that finds the parameters of a fuzzy system (i.e., fuzzy sets, fuzzy rules) by exploiting approximation techniques from neural networks.
Contents
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* 1 Combining fuzzy systems with neural networks
* 2 Characteristics
* 3 Cooperative Fuzzy Neural Network
* 4 Hybrid Fuzzy Neural Network
* 5 References
* 6 Recommended reading

Combining fuzzy systems with neural networks

Both neural networks and fuzzy systems have some things in common. They can be used for solving a problem (e.g. pattern recognition, regression or density estimation) if there does not exist any mathematical model of the given problem. They solely do have certain disadvantages and advantages which almost completely disappear by combining both concepts.

Neural networks can only come into play if the problem is expressed by a sufficient amount of observed examples. These observations are used to train the black box. On the one hand no prior knowledge about the problem needs to be given. On the other hand, however, it is not straightforward to extract comprehensible rules from the neural network's structure.

On the contrary, a fuzzy system demands linguistic rules instead of learning examples as prior knowledge. Furthermore the input and output variables have to be described linguistically. If the knowledge is incomplete, wrong or contradictory, then the fuzzy system must be tuned. Since there is not any formal approach for it, the tuning is performed in a heuristic way. This is usually very time consuming and error-prone.
Table 1: Comparison of neural control and fuzzy control Neural Networks Fuzzy Systems
no mathematical model necessary no mathematical model necessary
learning from scratch apriori knowledge essential
several learning algorithms not capable to learn
black-box behavior simple interpretation and implementation

It is desirable for fuzzy systems to have an automatic adaption procedure which is comparable to neural networks. As it can be seen in Table 1, combining both approaches should unite advantages and exclude disadvantages.

More about neural networks

It is possible to train a neural network which means a neural nework can learn by itself.There is a sepcial mechanism for a neural network to learn that is by the weight of their nodes.They will adjust their nodes accordingly.That allows them to train.However there are several key areas which we can use neural networks.Applications including speech synthesis,diagnostic problems,medicine,rorbotic control are some of them.Most of the time neural networks are used to simulate ,research , develop .

Why AI?

Actually the benefit you can get from this technology cant be imagine.Because if we can use AI for any purpose it makes easier to do things.If you can get any support from artificial intelligence that means the work can be handle without any human instruction.However there are certain limitations with these technology as machines do not have the power to think though we can show machines can think .However neural networks are introduced to solve this problem.Because a neural network can learn by itself.It is sign of intelligence.Still AI has a long path to go.

What is the path of AI

Now most of the resarches are trying to use AI in almost all the fields.Because it will be add another dimension to software industry.AI can be used for any purpose and currently scientists trying to develop the capability of AI.Hoever it is not possible them make them like a real human brain.Because AI is not capable of thinking.It can logically map things inorder to take a result.Most of the time it is very much complex.

More about AI

The branch of computer science concerned with making computers behave like humans. The term was coined in 1956 by John McCarthy at the Massachusetts Institute of Technology. Artificial intelligence includes
# games playing: programming computers to play games such as chess and checkers
# expert systems : programming computers to make decisions in real-life situations (for example, some expert systems help doctors diagnose diseases based on symptoms)
# natural language : programming computers to understand natural human languages
# neural networks : Systems that simulate intelligence by attempting to reproduce the types of physical connections that occur in animal brains
# robotics : programming computers to see and hear and react to other sensory stimuli

Currently, no computers exhibit full artificial intelligence (that is, are able to simulate human behavior). The greatest advances have occurred in the field of games playing. The best computer chess programs are now capable of beating humans. In May, 1997, an IBM super-computer called Deep Blue defeated world chess champion Gary Kasparov in a chess match.

In the area of robotics, computers are now widely used in assembly plants, but they are capable only of very limited tasks. Robots have great difficulty identifying objects based on appearance or feel, and they still move and handle objects clumsily.

Natural-language processing offers the greatest potential rewards because it would allow people to interact with computers without needing any specialized knowledge. You could simply walk up to a computer and talk to it. Unfortunately, programming computers to understand natural languages has proved to be more difficult than originally thought. Some rudimentary translation systems that translate from one human language to another are in existence, but they are not nearly as good as human translators. There are also voice recognition systems that can convert spoken sounds into written words, but they do not understand what they are writing; they simply take dictation. Even these systems are quite limited -- you must speak slowly and distinctly.

In the early 1980s, expert systems were believed to represent the future of artificial intelligence and of computers in general. To date, however, they have not lived up to expectations. Many expert systems help human experts in such fields as medicine and engineering, but they are very expensive to produce and are helpful only in special situations.

Today, the hottest area of artificial intelligence is neural networks, which are proving successful in a number of disciplines such as voice recognition and natural-language processing.

There are several programming languages that are known as AI languages because they are used almost exclusively for AI applications. The two most common are LISP and Prolog.

More about AI

The branch of computer science concerned with making computers behave like humans. The term was coined in 1956 by John McCarthy at the Massachusetts Institute of Technology. Artificial intelligence includes
# games playing: programming computers to play games such as chess and checkers
# expert systems : programming computers to make decisions in real-life situations (for example, some expert systems help doctors diagnose diseases based on symptoms)
# natural language : programming computers to understand natural human languages
# neural networks : Systems that simulate intelligence by attempting to reproduce the types of physical connections that occur in animal brains
# robotics : programming computers to see and hear and react to other sensory stimuli

Currently, no computers exhibit full artificial intelligence (that is, are able to simulate human behavior). The greatest advances have occurred in the field of games playing. The best computer chess programs are now capable of beating humans. In May, 1997, an IBM super-computer called Deep Blue defeated world chess champion Gary Kasparov in a chess match.

In the area of robotics, computers are now widely used in assembly plants, but they are capable only of very limited tasks. Robots have great difficulty identifying objects based on appearance or feel, and they still move and handle objects clumsily.

Natural-language processing offers the greatest potential rewards because it would allow people to interact with computers without needing any specialized knowledge. You could simply walk up to a computer and talk to it. Unfortunately, programming computers to understand natural languages has proved to be more difficult than originally thought. Some rudimentary translation systems that translate from one human language to another are in existence, but they are not nearly as good as human translators. There are also voice recognition systems that can convert spoken sounds into written words, but they do not understand what they are writing; they simply take dictation. Even these systems are quite limited -- you must speak slowly and distinctly.

In the early 1980s, expert systems were believed to represent the future of artificial intelligence and of computers in general. To date, however, they have not lived up to expectations. Many expert systems help human experts in such fields as medicine and engineering, but they are very expensive to produce and are helpful only in special situations.

Today, the hottest area of artificial intelligence is neural networks, which are proving successful in a number of disciplines such as voice recognition and natural-language processing.

There are several programming languages that are known as AI languages because they are used almost exclusively for AI applications. The two most common are LISP and Prolog.

What is Artificial intelligence

Artificial intelligence (AI) is the intelligence of machines and the branch of computer science which aims to create it. Major AI textbooks define the field as "the study and design of intelligent agents,"[1] where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success.[2] John McCarthy, who coined the term in 1956,[3] defines it as "the science and engineering of making intelligent machines."[4]

The field was founded on the claim that a central property of human beings, intelligence—the sapience of Homo sapiens—can be so precisely described that it can be simulated by a machine.[5] This raises philosophical issues about the nature of the mind and limits of scientific hubris, issues which have been addressed by myth, fiction and philosophy since antiquity.[6] Artificial intelligence has been the subject of breathtaking optimism,[7] has suffered stunning setbacks[8] and, today, has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science.[9]

AI research is highly technical and specialized, so much so that some critics decry the "fragmentation" of the field.[10] Subfields of AI are organized around particular problems, the application of particular tools and around long standing theoretical differences of opinion. The central problems of AI include such traits as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects.[11] General intelligence (or "strong AI") is still a long term goal of (some) research.[12]