基于最小二乘支持向量机的非线性系统建模

探讨了利用支持向量机进行非线性系统建模的方法。首先,利用相空间重构,将非线性时间数据序列映射到高维空间,以便把时间序列中蕴藏的信息充分显露出来。其次,基于最小二乘支持向量机(RLS-SVM)对系统进行建模,仿真结果表明,支持向量机具有良好的非线性建模能力和泛化能力,原始时间数据序列和重建时间数据序列相似,说明提出的算法能够有效的对非线性动态系统的时间序列进行建模。

Modelling of Nonlinear Systems Based on Recurrent Least Squares Support Vector Machines

An identification method for nonlinear systems using support vector machine was investigated. First, more information is acquired utilizing the reconstructed embedding phase space. Then, based on the Recurrent Least Squares Support Vector Machines (RLS-SVM), modeling of the nonlinear systems was carried out. The simulation result shows that the support vector machine has a good generalization ability and capability of modeling nonlinear process,The similarity of dynamic invariants between the origin and generated time series shows that the proposed method can capture the dynamics of the nonlinear systems series effectively.