Theoretic design of differential minimax controllers for stochastic cellular neural networks

Authors

Ziqian Liu, SUNY Maritime College
Henri Schurz, College of Engineering, Computing, Technology, and Mathematics
Nirwan Ansari, Newark College of Engineering
Qunjing Wang, Anhui University

Document Type

Article

Publication Date

2-1-2012

Abstract

This paper presents a theoretical design of how a minimax equilibrium of differential game is achieved in stochastic cellular neural networks. In order to realize the equilibrium, two opposing players are selected for the model of stochastic cellular neural networks. One is the vector of external inputs and the other is the vector of internal noises. The design procedure follows the nonlinear H infinity optimal control methodology to accomplish the best rational stabilization in probability for stochastic cellular neural networks, and to attenuate noises to a predefined level with stability margins. Three numerical examples are given to demonstrate the effectiveness of the proposed approach. © 2011 Elsevier Ltd.

Identifier

84855942374 (Scopus)

Publication Title

Neural Networks

External Full Text Location

https://doi.org/10.1016/j.neunet.2011.09.003

e-ISSN

18792782

ISSN

08936080

PubMed ID

22000751

First Page

110

Last Page

117

Volume

26

Recommended Citation

Liu, Ziqian; Schurz, Henri; Ansari, Nirwan; and Wang, Qunjing, "Theoretic design of differential minimax controllers for stochastic cellular neural networks" (2012). Faculty Publications. 18374.
https://digitalcommons.njit.edu/fac_pubs/18374