Performance evaluation of feed-forward neural network with soft computing techniques for hand written English alphabets

This paper describes the performance evaluation for the feed forward neural network with three different soft computing techniques to recognition of hand written English alphabets. Evolutionary algorithms for the hybrid neural network are showing the numerous potential in the field of pattern recognition. We have taken five trials and two networks of each of the algorithm: back propagation, Evolutionary algorithm, and Hybrid Evolutionary algorithm respectively. These algorithms have been taken the definite lead on the conventional approaches of neural network for pattern recognition. It has been analyzed that the feed forward neural network by two Evolutionary algorithms makes better generalization accuracy in character recognition problems. The problem of not convergence the weight in conventional backpropagation has also eliminated by using the soft computing techniques. It has been observed that, there are more than one converge weight matrix in character recognition for every training set. The results of the experiments show that the hybrid evolutionary algorithm can solve challenging problem most reliably and efficiently. These algorithms have also been compared on the basis of time and space complexity for the training set. (C) 2010 Elsevier B.V. All rights reserved.