Wavelet Dual Function-Link Fuzzy Brain Emotional Learning System Design for System Identification and Trajectory Tracking of Nonlinear Systems

Abstract

This paper proposes a new efficient identification system for nonlinear systems. The proposed wavelet dual function-link fuzzy brain emotional learning system (WDFLFBELS) is used as an identifier to identify the system and to track the trajectory of nonlinear systems. The WDFLFBELS consists of three sub-structures and a fuzzy inference system. The sub-structures include a prefrontal cortex, an amygdala, and a new dual function-link network, then it can efficiently reduce the identification and tracking errors, and obtain good performance. The gradient descent technique is used to find the adaptive laws to online tune the parameters of the system effectively. Simulation studies for identifying a time-varying system and tracking a chaotic trajectory are performed to validate the effectiveness and superiority of the proposed method.