Support Vector Regression and Immune Clone Selection Algorithm for Intelligent Electronic Circuit Fault Diagnosis

In the analysis of electronic circuit fault diagnosis based on support vector regression (SVR), irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper used rough sets as a preprocessor of SVR to select a subset of input variables and employed the immune clone selection algorithm (ICSA) to optimize the parameters of SVR. Additionally, the proposed ICSA-SVR model that can automatically determine the optimal parameters was tested on the prediction of electronic circuit fault. Then, we compared the proposed ICSA-SVR model with other artificial intelligence models of (BPN and fix-SVR). The experiment indicates that the proposed method is quite effective and ubiquitous.