Modified Mean Square Error with Regularization Algorithm for Efficient Classification of patterns in Back-propagation Neural Network

Abstract “Learn by example” concept utilizes Neural Network where each sample is fed to the network. Characters are used every day either in the form of Signature, Number plate,Name plate,Postal card address recognition and many more which can be employed by using Neural Network. This research investigates the suitability of using backpropagation neural network for the task of Off-line Hand written Character Recognition.This paper utilizesnew mean square error with regularization function for implementing Backpropagation neural network while employing hand written characters. The modified MSEREG have shown optimal results in the field of convergence rate, training time, simulation time and performance. The data's are implemented using MATLAB simulator.