Modified Mean Square Error Algorithm with Reduced Cost of Training and Simulation Time for Character Recognition in Backpropagation Neural Network
Neural Network concept is based on “Learn by example”. Mean square error function is the basic performance function which affects the network directly. Reducing of such error will result in an efficient system. The paper proposes a modified mean squared error value while training Backpropagation (BP) neural networks. The new cost function is referred as Arctan mean square error (AMSE).The formula computed prove that the modification of MSE is optimal in the sense of reducing the value of error for an asymptotically large number of statistically independent training data patterns. The results shows improved network with reduced error value along with increment in performance consequently.
KeywordsBackpropagation algorithm Mean square error algorithm neural network
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