Study of Hopfield neural network with sub-optimal and random GA for pattern recalling of English characters

In this paper we are studying the performance of Hopfield neural network for recalling of memorized patterns from the Hebbian rule and genetic algorithm for English characters. In this process the genetic algorithm is employed in random form and sub-optimal form for recalling of memorized patterns corresponding to the presented noisy prototype input patterns. The objective of this study is to determine the optimal weight matrix for correct recalling corresponding to noisy form of the English characters. In this study the performance of neural network is evaluated in terms of the rate of success for recalling of noisy input patterns of the English characters with GA in two aspects. The first aspect reflects the random nature of the GA and the second one exhibits the suboptimal nature of the GA for its exploration. The simulated results demonstrate the better performance of network for recalling of the stored letters of English alphabets using genetic algorithm on the suboptimal weight matrix.

Figure optionsDownload as PowerPoint slideHighlights
► Performance of Hopfield Neural Network for recalling of English characters with GA is studied.
► GA is used as sub-optimal and random form for pattern recalling of English characters.
► Performance of sub-optimal GA is better than the random GA.