Neural network for control chart pattern recognition based on kernel principle component analysis

Abstract

Considering the problem that the abnormal features have great similarity so that simple structure and high precision modeling cannot be achieved,a control chart pattern recognition method based on Kernel Principal Component Analysis(KPCA) and neural network was proposed.Firstly,the kernel method was used to translate the nonlinear feature into a higher dimensional linear feature space.Secondly this feature was projected to lower dimensional feature space.Finally the BP neural network classifier was introduced to identify the control chart pattern.This method was verified through stochastic simulation.The result demonstrates that the model can cluster each control chart pattern effectively and improve recognition accuracy.