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A multi-objective artificial immune algorithm for parameter optimization in support vector machine
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DOI：10.1016/j.asoc.2009.11.003.png)
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Support vector machine (SVM) is a classification method based on the structured risk minimization principle. Penalize, C; and kernel, sigma parameters of SVM must be carefully selected in establishing an efficient SVM model. These parameters are selected by trial and error or man's experience. Artificial immune system (AIS) can be defined as a soft computing method inspired by theoretical immune system in order to solve science and engineering problems. A multi-objective artificial immune algorithm has been used to optimize the kernel and penalize parameters of SVM in this paper. In training stage of SVM, multiple solutions are found by using multi-objective artificial immune algorithm and then these parameters are evaluated in test stage. The proposed algorithm is applied to fault diagnosis of induction motors and anomaly detection problems and successful results are obtained. (c) 2009 Elsevier B.V. All rights reserved.
Keyword:
Support vector machine
Artificial immune system
Optimization
Fault diagnosis
Anomaly detection
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IF:
6.6
论文数:
1.4W
被引数:
4.8W
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Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors独立分量分析与支持向量机相结合的感应电机智能故障诊断
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