Design of an intelligent prediction based neural network controller for multi scroll chaotic systems
An indispensable part of the precise control of multi scroll chaotic systems, model identification has received increasing attention in recent years. Because of plant uncertainty and unmodeled dynamics, conventional control methods cannot guarantee a sufficiently high performance for stabilizing multi scroll chaotic systems. In an effort to tackle the matter better, we propose an intelligent controller called the adaptive neural network prediction based controller (NN PbC The specified neural network is trained with the system model, which is extracted from a time series. In actual practice, the data are divided into two sets. One set is used for training and the other set for testing. In fact, a generalized NN will perform well for both training and testing data. The prediction based control method is then applied to the obtained neural network model to stabilize the multiple equilibrium points. The stability of the closed loop system is proven. In addition, simulation examples on two typical multi scroll chaotic systems are presented to demonstrate the effectiveness of the proposed controller.
Applied Intelligence