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 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.

Keywords

• TSK fuzzy system
• interval type-2 wavelet function
• brain emotional learning control
• cerebellar model articulation controller
• 5D chaotic system
• Henon map time series