Trajectory tracking error using fractional order time-delay recurrent neural networks using Krasovskii-Lur’e functional for Chua’s circuit via inverse optimal control

J. Perez Padrón, J.P. Pérez Padrón, C.F. Mendez-Barrios, E.J. Gonzalez-Galvan

Abstract


This paper presents an application of a Fractional Order Time Delay Neural Networks to chaos synchronization. The two main methodologies, on which the approach is based, are fractional order time-delay recurrent neural networks and the fractional order  inverse optimal control for nonlinear systems. The problem of trajectory tracking is studied, based on the fractional order Lyapunov-Krasovskii and Lur’e theory, that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a reference function is obtained. The method is illustrated for the synchronization, the analytic results we present a trajectory tracking simulation of a fractional order time-delay dynamical network and the Fractional Order Chua’s circuits

Keywords


Trajectory Tracking, Fractional Order Time-Delay Recurrent Neural Network, Fractional Order Lyapunov-Krasovskii-Lur’e Analysis

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DOI: https://doi.org/10.31349/RevMexFis.66.98

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Revista Mexicana de Física

ISSN: 2683-2224 (on line), 0035-001X (print)

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