Wavelet Interval Type-2 Takagi-Kang-Sugeno Hybrid Controller for Time-Series Prediction and Chaotic Synchronization

Abstract

This paper presents a new hybrid neural network for time series prediction and chaotic synchronization. The proposed controller consists of a wavelet interval type-2 Takagi Sugeno Kang fuzzy brain emotional learning controller (WIT2TFBELC), a wavelet interval type-2 Takagi Sugeno Kang fuzzy cerebellar model articulation controller (WIT2TFCMAC), and a robust compensator (RB). The main controller combines the WIT2TFBELC and the WIT2TFCMAC. A TSK fuzzy system is used to create a hybrid structure with the WIT2TFBELC and the WIT2TFCMAC. The TSK fuzzy system can effectively adjust the weighting for the main controller to achieve the time series prediction and chaotic prediction with better tracking response. Morever a robust compensator is used to achieve robust ability of the system. A Lyapunov function was used to establish the adaptive laws and effectively adjust the system parameters online. Finally, two examples of the application of the proposed algorithm are presented to point out the performance of proposed method.