6533b7d8fe1ef96bd126a48b

RESEARCH PRODUCT

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

Lei ShiYonggui KaoHamid Reza KarimiJing Xie

subject

Artificial neural networkMarkov chainCognitive NeuroscienceTransition rate matrixMarkov ChainsMarkovian jumpLyapunov functionalExponential stabilityArtificial IntelligenceControl theoryFuzzy cellular neural networksApplied mathematicsNeural Networks ComputerEquilibrium solutionAlgorithmsMathematics

description

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.

yearjournalcountryeditionlanguage
2014-05-08Neural Networks
https://doi.org/10.1016/j.neunet.2014.10.009