Adaptive learning based improved performance of activation functions in hidden layer using artificial neural network
Advancement in Artificial Neural Network always playing vital role in complex pattern recognition system. In this research work im-proved performance of the activation functions for the hidden layer in artificial neural network supervised by backpropagation algorithm with adaptive learning feature has been recorded for pattern recognition. Complexity of large data of pattern such as face recognition, cancer detection, object recognition, number plate surveillance etc is increasing day by day. To resolve complexity, performance of hid-den layer is registered using Log-sigmoid, Tan-sigmoid and purelin activation functions respectively due to their inherent properties. An excellent neural network training model of 1460 Alpha-Numeric data set with 3000 Epoch (iterations) have been trained in neural net-work through activation functions for number plate recognition. Hence the performance efficiency of hidden layer activation functions is recorded for pruning the overall back propagation neural network architecture with improved learning rate along with better time com-plexity for pattern matching.
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