Sensitivity analysis for conic section function neural networks

Abstract

Sensitivity analysis is a method for extracting 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 having on the network output. The basic idea is that each input channel to the network is offset slightly and the corresponding change in the output(s) is reported. 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. Furthermore, this may also improve the network performance. In this work, sensitivity analysis for Conic Section Function Neural Network is investigated and the results are given for different problems.