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Get Free AccessContinuous-time semi-Markovian jump neural networks (semi-MJNNs) are those MJNNs whose transition rates are not constant but depend on the random sojourn time. Addressing stochastic synchronization of semi-MJNNs with time-varying delay, an improved stochastic stability criterion is derived in this paper to guarantee stochastic synchronization of the response systems with the drive systems. This is achieved through constructing a semi-Markovian Lyapunov-Krasovskii functional together as well as making use of a novel integral inequality and the characteristics of cumulative distribution functions. Then, with a linearization procedure, controller synthesis is carried out for stochastic synchronization of the drive-response systems. The desired state-feedback controller gains can be determined by solving a linear matrix inequality-based optimization problem. Simulation studies are carried out to demonstrate the effectiveness and less conservatism of the presented approach.
Yanling Wei, Ju H. Park, Hamid Reza Karimi, Yu‐Chu Tian, Ho-Youl Jung (2017). Improved Stability and Stabilization Results for Stochastic Synchronization of Continuous-Time Semi-Markovian Jump Neural Networks With Time-Varying Delay. IEEE Transactions on Neural Networks and Learning Systems, 29(6), pp. 2488-2501, DOI: 10.1109/tnnls.2017.2696582.
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Type
Article
Year
2017
Authors
5
Datasets
0
Total Files
0
Language
English
Journal
IEEE Transactions on Neural Networks and Learning Systems
DOI
10.1109/tnnls.2017.2696582
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