title: Global exponential stability of delayed Markovian jump fuzzy cellular neural networks with generally incomplete transition probability.
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.
authors.affiliation: School of Science, Harbin Institute of Technology, Weihai, 264209, PR China. Electronic address: ygkao2008@gmail.com.
authors.forename: Yonggui
authors.initials: Y
authors.lastname: Kao
authors.affiliation: School of Science, Harbin Institute of Technology, Weihai, 264209, PR China.
authors.forename: Lei
authors.initials: L
authors.lastname: Shi
authors.affiliation: College of Information Science and Engineering, Ocean University of China, Qingdao, 266071, PR China.
authors.forename: Jing
authors.initials: J
authors.lastname: Xie
authors.affiliation: Department of Engineering, Faculty of Technology and Science, University of Agder, N-4898 Grimstad, Norway.
authors.forename: Hamid Reza
authors.initials: HR
authors.lastname: Karimi
mesh_terms: D000465:Algorithms; D008390:Markov Chains; D016571:Neural Networks, Computer
keywords: Fuzzy cellular neural networks; Generally uncertain transition rate; Global exponential stability; Markovian jumping parameters