Control of discrete chaotic systems based on echo state network modeling with an adaptive noise canceler

In this paper, we present a new method based on echo state network (ESN) to control discrete chaotic systems. ESN could achieve very high precision in chaotic time series prediction and overcome most issues encountered in using traditional artificial neural networks, especially local minima and overfitting. In order to achieve good control effect when there is noise in chaotic systems, an adaptive noise canceler is introduced to eliminate the effect of the noise and perturbation. The support vector machine (SVM) is adopted to identify inverse model of the controlled plant as the adaptive noise canceler. Simulation results show that the proposed method could achieve very good control effect, possess a good stability and completely reduce the adverse effect.