基于模糊遗传优化支持向量机的系统辨识研究

Research on System Identification Based on Fuzzy GAs Support Vector Machine

在实际应用中支持向量机的参数选取问题一直没有得到很好地解决,限制了其应用。为了能够自动获取最优的支持向量机参数,提出了基于模糊遗传算法的SVM参数选择方法,用模糊逻辑在线调整遗传算法的交叉概率pc和变异概率pm,并采用基于模糊遗传优化的支持向量机回归和BP神经网络对非线性系统辨识问题进行了研究。仿真结果表明,在小样本情况下,支持向量机比神经网络具有更高的系统辨识精度和更好的泛化能力。

In practice, the problem on how to select parameters of SVM is not solved properly,so that its application is restricted. In order to get the optimal parameters of SVM automatically, a parameter selection approach based on fuzzy genetic algorithms （FGAs） is proposed in this paper. The nonlinear system identification is studied using the crossover probability Pc and mutation probability Pm of the on-line adjustment genetic algorithm based on fuzzy logic, the support vector regression based on FGAs and BP neural network. The simulation results show that SVM has higher system identification precision and better generalization performance than neural networks in the case of small samples.