Global exponential stability of delayed Markovian jump fuzzy cellular neural networks with generally incomplete transition probability

Yonggui Kao; Lei Shi; Jing Xie; Hamid Reza Karimi

doi:10.1016/j.neunet.2014.10.009

Global exponential stability of delayed Markovian jump fuzzy cellular neural networks with generally incomplete transition probability

Neural Netw. 2015 Mar:63:18-30. doi: 10.1016/j.neunet.2014.10.009. Epub 2014 Nov 4.

Authors

Yonggui Kao¹, Lei Shi², Jing Xie³, Hamid Reza Karimi⁴

Affiliations

¹ School of Science, Harbin Institute of Technology, Weihai, 264209, PR China. Electronic address: ygkao2008@gmail.com.
² School of Science, Harbin Institute of Technology, Weihai, 264209, PR China.
³ College of Information Science and Engineering, Ocean University of China, Qingdao, 266071, PR China.
⁴ Department of Engineering, Faculty of Technology and Science, University of Agder, N-4898 Grimstad, Norway.

PMID: 25436485
DOI: 10.1016/j.neunet.2014.10.009

Abstract

The problem of global exponential stability in mean square of delayed Markovian jump fuzzy cellular neural networks (DMJFCNNs) with generally uncertain transition rates (GUTRs) is investigated in this paper. In this GUTR neural network model, each transition rate can be completely unknown or only its estimate value is known. This new uncertain model is more general than the existing ones. By constructing suitable Lyapunov functionals, several sufficient conditions on the exponential stability in mean square of its equilibrium solution are derived in terms of linear matrix inequalities (LMIs). Finally, a numerical example is presented to illustrate the effectiveness and efficiency of our results.

Keywords: Fuzzy cellular neural networks; Generally uncertain transition rate; Global exponential stability; Markovian jumping parameters.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Markov Chains
Neural Networks, Computer*