Open Access

ARTICLE

Identification of Abnormal Patterns in AR (1) Process Using CS-SVM

Hongshuo Zhang¹, Bo Zhu^1,*, Kaimin Pang¹, Chunmei Chen¹, Yuwei Wan²

1 College of Mechanical and Electrical Engineering, Kunming University of Science & Technology, Kunming, 650500, China
2 School of Engineering, Cardiff University, Cardiff, CF24 3AA, UK

* Corresponding Author: Bo Zhu. Email:

Intelligent Automation & Soft Computing 2021, 28(3), 797-810. https://doi.org/10.32604/iasc.2021.017232

Received 24 January 2021; Accepted 01 March 2021; Issue published 20 April 2021

Abstract

Using machine learning method to recognize abnormal patterns covers the shortage of traditional control charts for autocorrelation processes, which violate the applicable conditions of the control chart, i.e., the independent identically distributed (IID) assumption. In this study, we propose a recognition model based on support vector machine (SVM) for the AR (1) type of autocorrelation process. For achieving a higher recognition performance, the cuckoo search algorithm (CS) is used to optimize the two hyper-parameters of SVM, namely the penalty parameter c and the radial basis kernel parameter g. By using Monte Carlo simulation methods, the data sets containing samples of eight patters are generated in experiments for verifying the performance of the proposed model. The results of comparison experiments show that the average recognition rate of the proposed model reaches 96.25% as the autocorrelation coefficient is set equal to 0.5. That is apparently higher than those of the SVM model optimized by the particle swarm optimization (PSO) or the genetic algorithm (GA). Another experiment result demonstrates that the average recognition accuracy of the CS-SVM model also reaches higher than 95% for different autocorrelation levels. At last, a lot of data streams in or out of control are simulated to measure the ARL values. The results turn out that the model has an acceptable online performance. Therefore, we believe that the model can be used as a more effective approach for identification of abnormal patterns in autocorrelation process.

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