COMPUTATIONAL INTELEGENCES
associated neurons. A single neuron can be connected to many other neurons and the total number of neurons and connection in a network can be extremely large. Artificial neural network is a crude electronic model based on neural structure of the brain. The brain stores information as patterns utilizes these patterns and solves problems. This paper species that how the human brain learns? And also that defines the essential
Features of neurons and their interconnections. We then typically program a computer to simulate these features. In this paper we have shown theoretically neural network versus conventional computers. Neural networks are based as they are on a crude low level model of biological neural systems. In future, the development of this neurobiological modeling may lead to intelligent genuinely computers.
This paper is discussed the following points:-
1. Artificial neural network
2. Neural Networks versus Conventional Computers
3. Applications of ANN
4. Limitations of Neuro- Fuzzy Systems
5. Conclusion
It is a fairly new name covering a fairly new field. Computational Intelligence is a branch of study of artificial Intelligence. CI is a set of machine capabilities which allowed a machine to use acquired knowledge and to solve new task efficiently in the presence of new condition of their performance. Computational intelligence is the study of the design of intelligence agents. An ‘agent’ is something that acts in an environment such as mobile, robot, a web Crawler, an automated medical diagnosis system.Some techniques within computational intelligence are often counted as artificial intelligence techniques(i.e., genetic algorithm, neural network ) there is a cleared difference between these techniques and traditional logic based artificial intelligence techniques. Theoretical and computational neuroscience is the field concerned with the theoretical analysis and computational modeling of biological neural system. Neural networks are based as they are on a crude low level model of biological neural systems. In future, the development of this neurobiological modeling may lead to intelligent genuinely computers
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