参数选择对于支持向量机(support vector machine, SVM)的分类性能很重要，其本质是搜索寻优.该文提出以最小化K-fold交叉验证误差为目标，以改进模拟退火粒子群优化算法(improved simulated annealing particle swarm optimization, IM-SAPSO)为寻优方法的SVM参数优化方法. 利用优化的SVM对扩展的二元相移键控(extended binary phase shift keying, EBPSK)通信系统中经冲击滤波器的“0”和“1”码元进行分类，并和基于SVM、PSO-SVM以及幅度积分判决的EBPSK检测器进行性能对比. 仿真结果表明：基于IMSAPSO和SVM的EBPSK检测器性能明显好于其他3 种检测器.

Parameter selection is important to the classification performance of support vector machine (SVM),which is essentially a search of optimum. This paper proposes a parameter selection method for SVM with the algorithm of improved simulated annealing particle swarm optimization (IM-SAPSO) to search the best parameters. The minimized K-fold cross-validation error is used as the object of IM-SAPSO. The optimized SVM is then used to classify the symbols 0 and 1 passing the impacting filter of an extended binary phase shift keying (EBPSK) communication system. Comparison is made for the detection performance of EBPSK detector between the proposed IM-SAPSO and other methods including those based on SVM, PSO-SVM and amplitude integral decision. Simulation results show that IM-SAPSO and SVM are significantly better than the other three methods.