A neuro-genetic Hopfield associative memory (HAM) with improved noise immunity

Abstract

Artificial Neural Network (ANN) is a form of computation inspired by the arrangement and purpose of the brain. Pattern Recognition is one of the demanding areas under Artificial Neural Network and pattern association is one of the major tasks through which pattern recognition is realized. Nowadays researchers are taking interest in the combination of evolutionary algorithms and ANN. In the present work, an attempt is being made to analyze the performance of Hopfield Associative Memory with Monte Carlo adaptation rule and evolutionary searching (Genetic Algorithm) for pattern storage and recall. The training pattern set has been taken a set of five Greek alphabet letters as different objects. The aim is to store Patterns with MC adaptation rule and get the best weight matrices for efficient recalling of any given set patterns. The neural networks architectures have been taken up for storing the given set of inputs, for which conventional Hebbian and Monte Carlo adaptation rule is considered. During the mutation process, various mutation probabilities are supposed to be evaluated. The detailed simulated results thus obtained will be presented with the help of tables and graph.