Hopfield Neural Network as Associated Memory with Monte Carlo- (MC-)Adaptation Rule and Genetic Algorithm for pattern storage

This paper describes the performance analysis of Hopfield neural networks by usinggenetic algorithm and Monte Carlo-(MC-) adaptation learning rule.A set of five objects has been considered as the pattern set. In the Hopfield type of neural networks of associative memory, the weighted code of input patterns provides an auto-associative function in the network. The storing of the objects has been performed using Hebbian rule and recalling of these stored patterns on presentation of prototype input patterns has been made using both-conventional hebbian rule and geneticalgorithm.In most cases, the recalling of patterns using genetic algorithm with MC-adaptation rule seems to give better results than the conventional hebbian rule, MC-adaptation rule and simple genetic algorithm recalling techniques