Finite-Time Synchronizing Control for Chaotic Neural Networks

Chao Zhang, Qiang Guo and Jing Wang

Abstract and Applied Analysis, 2014, vol. 2014, 1-9

Abstract:

This paper addresses the finite-time synchronizing problem for a class of chaotic neural networks. In a real communication network, parameters of the master system may be time-varying and the system may be perturbed by external disturbances. A simple high-gain observer is designed to track all the nonlinearities, unknown system functions, and disturbances. Then, a dynamic active compensatory controller is proposed and by using the singular perturbation theory, the control method can guarantee the finite-time stability of the error system between the master system and the slave system. Finally, two illustrative examples are provided to show the effectiveness and applicability of the proposed scheme.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlaaa:938612

DOI: 10.1155/2014/938612

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