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Stochastic finite-time boundedness of Markovian jumping neural network with uncertain transition probabilities. (English) Zbl 1219.93143
Summary: The stochastic finite-time boundedness problem is considered for a class of uncertain Markovian jumping neural networks (MJNNs) that possess partially known transition jumping parameters. The transition of the jumping parameters is governed by a finite-state Markov process. By selecting the appropriate stochastic Lyapunov-Krasovskii functional, sufficient conditions of stochastic finite time boundedness of MJNNs are presented and proved. The boundedness criteria are formulated in the form of linear matrix inequalities and the designed algorithms are described as optimization ones. Simulation results illustrate the effectiveness of the developed approaches.
MSC:
93E15Stochastic stability
60J27Continuous-time Markov processes on discrete state spaces
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