Stabilization of stochastic recurrent neural networks via inverse optimal control

2003 ◽  
Author(s):  
E.N. Sanchez ◽  
J.P. Perez ◽  
Guanrong Chen
Keyword(s):  
Neural Networks ◽  
Optimal Control ◽  
Recurrent Neural Networks ◽  
Inverse Optimal Control

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

2019 ◽  
Vol 66 (1) ◽  
pp. 98
Author(s):  
J. Perez Padrón ◽  
J.P. Pérez Padrón ◽  
C.F. Mendez-Barrios ◽  
E.J. Gonzalez-Galvan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Optimal Control ◽  
Time Delay ◽  
Fractional Order ◽  
Trajectory Tracking ◽  
Recurrent Neural Networks ◽  
Chaos Synchronization ◽  
Tracking Error ◽  
Inverse Optimal Control

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

scholarly journals USING DYNAMIC NEURAL NETWORKS TO GENERATE CHAOS: AN INVERSE OPTIMAL CONTROL APPROACH

2001 ◽  
Vol 11 (03) ◽  
pp. 857-863 ◽  
Author(s):  
EDGAR N. SANCHEZ ◽  
JOSE P. PEREZ ◽  
GUANRONG CHEN
Keyword(s):  
Neural Networks ◽  
Optimal Control ◽  
Chaotic Attractors ◽  
Control Law ◽  
Inverse Optimal Control ◽  
Dynamic Neural Network ◽  
Dynamic Neural Networks ◽  
New Approach ◽  
Control Approach ◽  
Optimal Control Approach

This Letter suggests a new approach to generating chaos via dynamic neural networks. This approach is based on a recently introduced methodology of inverse optimal control for nonlinear systems. Both Chen's chaotic system and Chua's circuit are used as examples for demonstration. The control law is derived to force a dynamic neural network to reproduce the intended chaotic attractors. Computer simulations are included for illustration and verification.

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