Nonlinear System Identification using Least Squares Support Vector Machines

Abstract

Support vector machines (SVM) is a novel machine learning method based on small-sample statistical learning theory (SLT), and is powerful for the problem with small sample, nonlinearity, high dimension, and local minima. SVM have been very successful in pattern recognition, fault diagnoses and function estimation problems. Least squares support vector machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function. This paper discusses least squares support vector machines (LS-SVM) estimation algorithm and introduces applications of the novel method for the nonlinear control systems. Then identification of MIMO models and soft-sensor modeling based on least squares support vector machines (LS-SVM) is proposed. The simulation results show that the proposed method provides a powerful tool for identification and soft-sensor modeling and has promising application in industrial process applications