Input-to-state stabilization of dynamic neural networks

Authors:
E. N. Sanchez;J. P. Perez
Affiliations:
CINVESTAV, Unidad Guadalajara, Mexico;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Year:
2003

Citing 0
Cited 5

Global robust stabilizing control for a dynamic neural network system

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Exponential stabilization of neural networks with various activation functions and mixed time-varying delays

IEEE Transactions on Neural Networks
Nonlinear system stabilisation by an evolutionary neural network

ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Nonlinear discrete system stabilisation by an evolutionary neural network

IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Exponential stabilization of delayed recurrent neural networks: A state estimation based approach

Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

As a continuation of their previous published results, in this paper the authors propose a new methodology, for input-to-state stabilization of a dynamic neural network. This approach is developed on the basis of the recent introduced inverse optimal control technique for nonlinear control. An example illustrates the applicability of the proposed approach.