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

This paper presents a new hybrid neural network controller for time series prediction and chaotic synchronization. The proposed controller is called as a wavelet interval type-2 Takagi-Kang-Sugeno (TSK) fuzzy brain emotional learning cerebellar model articulation controller (WIT2TFBCC), and it consists of a wavelet interval type-2 TSK fuzzy brain emotional learning controller (WIT2TFBELC), and a wavelet interval type-2 TSK fuzzy cerebellar model articulation controller (WIT2TFCMAC). The proposed WIT2TFBCC can serve both as a control signal for chaotic master-slave synchronization and as a prediction output signal for the time series predictor. Moreover, 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 are presented to illustrate the performance of proposed method.

Automated summary

This paper presents a new hybrid neural network controller for time series prediction and chaotic synchronization. The proposed controller consists of a wavelet interval type-2 TSK fuzzy brain emotional learning controller and a wavelet interval type-2 TSK fuzzy cerebellar model articulation controller. It can serve as both a control signal for chaotic master-slave synchronization and a prediction output signal for time series prediction. The use of a robust compensator helps achieve robustness in the system. The adaptive laws based on Lyapunov function effectively adjust system parameters online.