Handwritten English Vowels Recognition Using Hybrid Evolutionary Feed-forward Neural Network

Abstract

This paper compares the performance of Backpropagation algorithm with the hybrid evolutionary algorithm (EA) in feed-forward neural networks. The analysis is done with five different samples of handwritten English language vowels. These characters are presented to the neural network for training. The training in the neural network is performed by adjusting the connection strength in it. The evolutionary algorithms evolve the population of weights of the neural network during the training. Using a simulator program, which is designed in C & MATLAB, each algorithm was compared by using five data sets of handwritten English language vowels. The 5 trials indicate significant difference between the two algorithms in the chosen data sets. The results show that the performance of the neural network is much accurate and convergent for the learning with the hybrid evolutionary algorithm.