Pattern recalling analysis of English alphabets using Hopfield model of feedback neural network with evolutionary searching

In this paper, we are analysing the performance of Hopfield model of feedback neural networks (NNs) with general Hebbian learning rule and genetic algorithm (GA) for pattern recognition. In the Hopfield type of NNs, the weighted code of input patterns provides an auto-associative function in the network, which exhibits its associative memory feature. The objective is to determine the optimal weight matrix for efficient recalling of any approximate input pattern. For this, we explore the population generation technique (mutation and elitism), crossover and setting up of proper fitness evaluation functions to generate the new population of the weight matrices. This process will continue until the last weight matrix has been selected. The experiments consider a neural network architecture that stores all letters of English alphabets (capitals only) using Hebbian rule and then accomplishes the recalling of these stored patterns on presentation of any prototype input pattern of the already stored patterns using both conventional Hebbian rule and evolutionary algorithm. The simulated results demonstrate the better performance of network for recalling of the stored letters of English alphabets using GA and minimise the randomness from the GA.