Master-Slave Synchronization for Trajectory Tracking Error Using Fractional Order Time-Delay Recurrent Neural Networks via Krasovskii-Lur’e Functional for Chua’s Circuit

J. Javier Perez D., José Paz Pérez Padron, Atilano Martínez Huerta, Joel Pérez Padron


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.


Trajectory tracking, fractional order time-Delay recurrent neural network, fractional order Lyapunov-Krasovskii and lur’e analysis

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