A Research about Pattern Recognition of Control Chart Using Optimized ANFIS and Selected Features

Abstract

Unnatural patterns in the control charts can be associated with a specific set of assignable causes for process variation. Hence pattern recognition is very useful in identifying process problem. This article introduces a novel hybrid intelligent system that includes three main modules: a feature extraction module, a classifier module, and an optimization module. In the feature extraction module, a proper set combining the shape features and statistical features is proposed as the efficient characteristic of the patterns. In the classifier module, adaptive neuro-fuzzy inference system (ANFIS)-based classifier is proposed. For the optimization module, cuckoo optimization algorithm (COA) is proposed to improve the generalization performance of the recognizer. In this module, it the ANFIS classifier design is optimized by searching for the best value of the parameter and looking for the best subset of features that feed the classifier. Simulation results show that the proposed algorithm has very high recognition accuracy (RA). This high efficiency is achieved with only little features, which have been selected using COA.