Performance Evaluation of Pattern Storage Network of Associative memory with Sub-optimal GA for Hand written Hindi SWARS

Abstract

In this paper we are performing the evaluation of Hopfield neural network as Associative memory for recalling of memorized patterns from the Sub-optimal genetic algorithm for Handwritten of Hindi language. In this process the genetic algorithm is employed from sub-optimal form for recalling of memorized patterns corresponding to the presented noisy prototype input patterns. The sub-optimal form of GA is considered as the non-random initial population or solution. So, rather than random start, the GA explores from the sum of correlated weight matrices for the input patterns of training set. The objective of this study is to determine the optimal weight matrix for correct recalling corresponds to approximate prototype input pattern of Hindi ‘SW. In this study the performance of neural network is evaluated in terms of the rate of success for recalling of memorized Hindi  for presented approximate prototype input pattern with GA in two aspects. The first aspect reflects the random nature of the GA and the second one exhibit the suboptimal nature of the GA for its exploration.The simulated results demonstrate the better performance of network for recalling of the memorized Hindi SWARS using genetic algorithm to evolve the population of weights from sub-optimal weight matrix.