【摘要】 支持向量机算法(SVM)具有可靠的全局最优性和良好的泛化能力,适用于有限样本的学习,而该算法的成功与否很大程度上取决于其参数的选择,而常规经验选取方法往往不能获得满意效果。利用粒子群算法(PSO)随机搜索策略对支持向量机参数进行优选,建立基于粒子群算法参数优化的支持向量机模型(PSO-SVM)。仿真结果表明,该优化模型比传统的人工神经网络(BP)模拟效果要好,在拟合精度方面有很大的提高,且具有较好的泛化能力。更多还原

【Abstract】 The support vector machine(SVM) algorithm is of reliable global optimality and good generalization,suitable for the learning of finite samples.However,the results considerably depend on the SVM model parameters and the conventional parameter choosing method by experience is unsatisfactory.Using the particle swarm optimization(PSO) random search strategy,we can establish the optimization parameters of support vector machine.It is shown that ACO-SVM is much better in the simulation results than the artificial neural network,which greatly improves in fitting precision,and it has good generalization ability.更多还原