Discovery of optimal neurons and hidden layers in feed-forward Neural Network

Abstract

Identifying the number of neurons in each hidden layers and number of hidden layers in a multi layered Artificial Neural Network (ANN) is a challenge based on the input data. A new hypothesis is proposed for organizing the synapse from x to y neuron. The synapse of number of neurons to fire between the hidden layer is identified. By the introduction of this hypothesis, an effective number of neurons in multilayered Artificial Neural Network can be identified and self organizing neural network model is developed which is referred as cognitron. The normal brain model has 3 layered perceptron; but the proposed model organizes the number of layers optimal for identifying an effective model. Our result proved that the proposed model constructs a neural model directly by identifying the optimal weights of each neurons and number of neurons in each dynamically identified hidden layers. This optimized model is self organized with different range of neurons on different layer of hidden layer, and by comparing the performance based on computational time and error at each iteration. An efficient number of neurons are organized using gradient decent. The proposed model thus train large model to perform the classification task by inserting optimal layers and neurons. � 2016 IEEE.