Abstract
In this manuscript, the synchronization of four-dimensional (4D) chaotic systems with uncertain parameters using a self-evolving recurrent interval type-2 Petri cerebellar model articulation controller is studied. The design of the synchronization control system is comprised of a recurrent interval type-2 Petri cerebellar model articulation controller and a fuzzy compensation controller. The proposed network structure can automatically generate new rules or delete unnecessary rules based on the self-evolving algorithm. Furthermore, the gradient-descent method is applied to adjust the proposed network parameters. Through Lyapunov stability analysis, bounded system stability is guaranteed. Finally, the effectiveness of the proposed controller is illustrated using numerical simulations of 4D chaotic systems.
Highlights
Chaotic synchronization has attracted academic attention due to its nonlinear phenomena characteristic
In 2016, Naderi and Kheiri proposed a secure-communication method using the exponential synchronization of a chaotic system [2]
To improve the work of [58], this paper incorporates the advantages of cerebellar model articulation controller (CMAC), interval type-2 fuzzy logic systems (IT2FLS), recurrent neural network (RNN), and
Summary
Chaotic synchronization has attracted academic attention due to its nonlinear phenomena characteristic. To improve the work of [58], this paper incorporates the advantages of CMAC, IT2FLS, RNN, and FPNs to propose a recurrent interval type-2 Petri cerebellar model articulation controller (RIT2PC). In 2017, Lin et al introduced a self-evolving function-link interval type-2 fuzzy neural network for nonlinear system identification and control [60]. The main contributions of this study include the following: successful development of a self-evolving RIT2PC (SRIT2PC) control system; the online learning-parameter adaptation laws are obtained using the gradient-descent method; the Lyapunov stability function is used to prove the stability of the proposed synchronization system; the effectiveness of the proposed control method is illustrated using numerical experiments of four-dimensional (4D) chaotic systems.
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