Support Vector Machine Based Modeling of Nonlinear Systems with Hysteresis

Support vector machine (SVM) is a brand-new machine learning technique based on statistical learning theory. It is an ideal facility for modeling of various nonlinear systems. Hysteresis phenomena are common in actuators and sensors, such as gears and saturation, which would undermine the stability of system and the accuracy of control badly. In this paper, a support vector machine based approach for modeling of systems with hysteresis is proposed, and an improved version is developed. The developed identification approaches are numerically implemented in Matlab simulation program, and the improved version is proved to be effective and more accurate than BP neural networks when being used for modeling of systems with hysteresis