Artificial neural network

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This seminar is about the artificial neural network application in processing industry. An artificial neural network as a computing system is made up of a number of simple and highly interconnected processing elements, which processes information by its dynamic state response to external inputs. In recent times study of ANN models have gained rapid and increasing importance because of their potential to offer solutions to some of the problems in the area of computer science and artificial intelligence. Instead of performing a program of instructions sequentially, neural network models explore many competing hypothesis simultaneously using parallel nets composed of many computational elements. No assumptions will be made because no functional relationship will be established. Computational elements in neural networks are non linear models and also faster. Hence the result comes out through non linearity due to which the result is very accurate than other methods. The details of deferent neural networks and their learning algorithm are presented its clearly illustrator how multi layer neural network identifies the system using forward and inverse modeling approach and generates control signal. The method presented here are directed inverse, direct adaptive, internal model and direct model reference control based ANN techniques. 

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