Inverse System Control of Nonlinear Systems Using LS-SVM

Abstract

This paper firstly provides a short introduction to least square support vector machine (LS-SVM), then provides sequential minimal optimization (SMO) based pruning algorithms for LS-SVM. After a simple discussion of inverse-model identification, a LS-SVM based direct-model identification method is developed by using LS-SVM's excellent ability of function approximation. The most important and difficult step in inverse control methods is the modeling of the inverse nonlinear dynamic system. Both SVM and LS-SVM can solve this problem. Simulation results demonstrate LS-SVM method is better than SVM in accuracy, static state performance as well as computer cost.