Simulation of weight pruning process in backpropagation neural network for pattern classification: A self-running threshold approach

摘要

Neural network minimization is a process in which non-affecting elements (non-contributing processing elements and weights) are deleted. This results in the network with a minimum configuration and positively contributing elements. In general, the scope of the minimization process is to achieve a network which provides optimal solution for a given task without loss of performance. Therefore, study into network minimization becomes essential for complicated tasks. The present study investigates ways of developing automatic thresholding methods for weight deleting process to minimize the network resulting in enhanced performances for classification of six control chart patterns.