An improved SVM classifier based on double chains quantum genetic algorithm and its application in analogue circuit diagnosis

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• 作者：Peng Chen ^{mtgh1990@163.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Lifen Yuan ; ^{yuanlifen_hfut@163.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Yigang He ^{18655136887@163.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Shuai Luo ^{hfut20094751@163.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Analog circuit diagnosis ; SVM ; DCQGA
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：26 October 2016
• 年：2016
• 卷：211
• 期：Complete
• 页码：202-211
• 全文大小：1462 K

文摘

Support Vector Machine (SVM) classifier is widely used in analogue circuit diagnosis. However, the penalty parameter C and the kernel parameter γ of SVM classifier with the radial basis function (RBF) affect the classification performance seriously. A double-chains-quantum-genetic-algorithm (DCQGA) based method is proposed to optimize C and γ. In DCQGA, each chromosome carries two gene chains, and each of gene chains represents an optimization solution, which can accelerate the search process and help to find the global solution. Thereafter, the optimal parameters C and γ are obtained by optimizing the parameter searching process with DCQGA. Two common datasets named Iris and Wine from UCI Machine Learning Repository are used to test the performance of the presented SVM classifier. The simulation results illustrate that the population's best fitness and the classifying accuracy of the proposed DCQGA-SVM are higher than that of the Particle-Swarm-Optimization based SVM (PSO-SVM), the Quantum Genetic Algorithm based SVM (QGA-SVM) and the classifier based on grid search method (GS-SVM). Finally, the proposed DCQGA-SVM is applied to analogue circuit diagnosis, a Sallen–Key bandpass filter circuit and a four-opamp biquad high-pass filter are chosen as circuits under test (CUT). Wavelet packet analysis is performed to extract the fault features before classifying. The experimental results show that the SVM parameters selected by DCQGA-SVM contribute to higher diagnosis accuracy than other methods referred in this paper.