New Approach for Feature Selection of Thermomechanically Processed HSLA Steel using Pruned-Modular Neural Networks

Abstract

A new approach has been used in modeling of strength and ductility of high strength low alloy (HSLA) steel, where a comparative study among fully-connected neural network, modular network and pruned-module architecture has been performed. The important features for modeling such a complex steel processing system have been worked out. Performance evaluation and feature selection in the soft computing domain are the two important activities for modeling input–output relationship. The need arises specially when the system is complex in terms of type of network architecture, number of features involved, number of inter-connections, application domain etc. In this paper, an attempt is made to develop a new metric of performance evaluation, using mean squared error and the total number of inter-connections of a network to improve the understanding about a complex system of thermomechanically controlled processed HSLA steels. The methodology for feature selection is developed next based on the functional form of output in terms of input variables where gradient of the function can be computed in the network.