Hopfield Neural Network as Associated Memory with Monte Carlo- (MC-)Adaptation Rule and Genetic Algorithm for pattern storage
In this paper, the authors describe the performance analysis of Hopfield neural networks by using genetic 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 genetic algorithm.