基于改进的LSSVM辨识动态非线性时变系统

针对非线性时变系统难以辨识的问题，提出了一种基于改进最小二乘支持向量机的辨识新方法。该方法在加权最小二乘支持向量机的基础上，引入用矢量基学习和自适应迭代相结合的方式得到一个小的支持向量，同时采用加权方法确定权值系数以减小训练样本中非高斯噪声的影响。通过对动态非线性时变系统的仿真，结果表明该算法具有较好的鲁棒性、支持向量稀疏性和动态建模实时性。

Identification of dynamic nonlinear systems based on modified LSSVM

According to the problem about difficult to identify the nonlinear time-varying system,a new identification meth-od is proposed based on modified Least Squares Support Vector Machines（LSSVM）.During the algorithm＇s training process,the vector base learning and automatic iterative procedures are introduced based on weighted least squares support vector machines,and a small support vector set can be obtained adaptively.Meanwhile the weights are determined by a robust method in order to reduce the effect of the outliers.Simulating for a nonlinear time-varying system,the result shows that the proposed method has a better robustness,sparseness of support vector and a real-time performance.