A Modified Grey Wolf Optimizer for Optimum Parameters of Multilayer Type-2 Asymmetric Fuzzy Controller

Abstract

This study presents a modified algorithm of the grey wolf optimizer to solve the problem of learning rate selection in the multilayer type-2 asymmetric fuzzy controller (MT2AFC). The improvements of our modified optimizer are: the best position of the swarm is memorized, thus making the alpha wolves only update when a better position appears in the next iteration; search performance is enhanced by giving more freedom to update the grey wolf position. The proposed optimizer algorithm is then applied to optimize the suitable learning rates for the proposed controller. The multilayer type-2 asymmetric membership function is used in the fuzzy control network to enhance the learning ability and flexibility of the designed network architecture. The gradient descent method is used to adjust the parameters of the proposed MT2AFC controller online. The stability of the system is guaranteed using the Lyapunov approach. Besides, the self-evolving algorithm is used to construct the network structure autonomously. Ultimately, the numerical simulations of the chaotic synchronization systems are carried out to verify the effectiveness of our proposed method. INDEX TERMS grey wolf optimizer, interval type-2 fuzzy neural network, asymmetric membership function, chaotic synchronization systems.

Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

“…Le et al [147] suggested an improved GWO to overcome the learning rate determination issue in the multilayer type-2 asymmetric fuzzy controller. Two phases were suggested for improving the GWO: remembering the swarm's optimal location and enhancing the ability of the agents to search.…”

Interval Type-2 Fuzzy Brain Emotional Control Design for the Synchronization of 4D Nonlinear Hyperchaotic Systems

“…for nonlinear chaotic systems to achieve good control performance such as an adaptive fuzzy control (Sambas et al 2020), a passive control (Sambas et al 2019a), an active backstepping control (Sambas et al 2021), an adaptive control (Sambas et al 2019b), an integral sliding mode control (Vaidyanathan et al 2019), a double function-link brain emotional control (Huynh et al 2020c), a modified grey wolfbased multilayer type-2 asymmetric fuzzy control (Le et al 2020b), a self-organizing interval type-2 fuzzy asymmetric cerebellar model articulation control (Le et al 2020a), a wavelet interval type-2 fuzzy brain emotional control (Huynh et al 2020a), and a brain-imitated neural network control .…”

Interval type-2 fuzzy brain emotional control design for the synchronization of 4D nonlinear hyperchaotic systems

“…for nonlinear chaotic systems to achieve good control performance such as an adaptive fuzzy control (Sambas et al 2020), a passive control (Sambas et al 2019a), an active backstepping control (Sambas et al 2021), an adaptive control (Sambas et al 2019b), an integral sliding mode control (Vaidyanathan et al 2019), a double function-link brain emotional control (Huynh et al 2020c), a modified grey wolfbased multilayer type-2 asymmetric fuzzy control (Le et al 2020b), a self-organizing interval type-2 fuzzy asymmetric cerebellar model articulation control (Le et al 2020a), a wavelet interval type-2 fuzzy brain emotional control (Huynh et al 2020a), and a brain-imitated neural network control .…”