Synchronization and state estimation for discrete-time coupled delayed complex-valued neural networks with random system parameters. (June 2022)

• Record Type: Journal Article
• Authors: Liu, Yufei; Shen, Bo; Zhang, Ping
• Is Part Of: Neural networks. Volume 150(2022)
• Page Start: 181
• Page End: 193
• Publication Date: 2022-06
• Subjects: Coupled complex-valued neural networks -- Discrete-time -- Random system parameters -- Synchronization -- State estimation; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2022.02.028
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: In this paper, an array of discrete-time coupled complex-valued neural networks (CVNNs) with random system parameters and time-varying delays are introduced. The stochastic fluctuations of system parameters, which are characterized by a set of random variables, are considered in the individual CVNNs. Firstly, the synchronization issue is solved for the considered coupled CVNNs. By the use of the Lyapunov stability theory and the Kronecker product, a synchronization criterion is proposed to guarantee that the coupled CVNNs are asymptotically synchronized in the mean square. Subsequently, the state estimation issue is studied for the identical coupled CVNNs via available measurement output. By establishing a suitable Lyapunov functional, sufficient conditions are derived under which the mean square asymptotic stability of the estimation error system is ensured and the design scheme of desired state estimator is explicitly provided. Finally, two numerical simulation examples are shown for the purpose of illustrating the effectiveness of the proposed theory.