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论文类型：会议论文

发表时间：2011-04-15

收录刊物：EI、Scopus

页面范围：3410-3413

摘要：Appropriate parameters are very crucial to the learning performance and generalization ability of least-squares support vector machines (LS-SVM). In this paper, a novel parameter selection method for LS-SVM is presented based on chaotic ant swarm (CAS) algorithm. The selection problem of LS-SVM parameters is considered as a compound optimization problem. Then objective function of optimization problem is set and a CAS optimization algorithm is employed to search optimal objective function. CAS algorithm is global search method and it need not to consider LS-SVM dimensionality and complexity. The simulation results show that the proposed method is an effective approach for parameter optimization and the good performance for function approximation is obtained. ? 2011 IEEE.