Neural Network and Fuzzy Logic

In the present world of science and technology networking has assumed great importance. As a matter of fact Neural Networks have arisen from analogies with models of the way that humans might approach pattern recognition tasks, although they have been made for these procedures and although few of these claims have withstood careful scrutiny, neural network methods have had great impact on pattern recognition practice. A Theoretical understanding of how they work is still under construction, and is attempted here by viewing neural networks within a statistical framework, together with methods developed in the field of machine learning. The present publication deals with the neural networks which are composed of simple elements operating in parallel. These elements are inspired by biological nervous systems. As in nature, the network function is determined largely by the connections between elements. We can train a neural network to perform a particular function by adjusting the values of the connections between elements. Fuzzy logic control has become an important methodology in control engineering.