Reduction of complexity in conic section function neural network

Abstract

This paper details how the complexity can be reduced in conic section function neural network (CSFNN) by using sensitivity analysis and the results are given for various problems. This is, particularly important for neural network hardware applications. The method used here extracts the cause and effect relationship between the inputs and outputs of the network. After training a neural network, one may want to know the effect that each of the network inputs is hating on the network output. The input channels that produce low sensitivity values can be considered insignificant and can most often be removed from the network. This will reduce the size of the network, which in turn reduces the complexity and the training time.