Neural-Impulsive Pinning Control for Complex Networks Based on V-Stability

Daniel Ríos-Rivera, Alma Y. Alanis and Edgar N. Sanchez
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Daniel Ríos-Rivera: Computer Sciences Department, Universidad de Guadalajara, Guadalajara 44430, Mexico
Alma Y. Alanis: Computer Sciences Department, Universidad de Guadalajara, Guadalajara 44430, Mexico
Edgar N. Sanchez: Electrical Engineering Department, CINVESTAV, Unidad Guadalajara, Zapopan 45017, Mexico

Mathematics, 2020, vol. 8, issue 9, 1-20

Abstract: In this work, a neural impulsive pinning controller for a twenty-node dynamical discrete complex network is presented. The node dynamics of the network are all different types of discrete versions of chaotic attractors of three dimensions. Using the V-stability method, we propose a criterion for selecting nodes to design pinning control, in which only a small fraction of the nodes is locally controlled in order to stabilize the network states at zero. A discrete recurrent high order neural network (RHONN) trained with extended Kalman filter (EKF) is used to identify the dynamics of controlled nodes and synthesize the control law.

Keywords: complex networks; V-stability; pinning control; impulsive control; neural control; chaos control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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