Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 29, Issue 6, Pages 2488-2501Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2017.2696582
Keywords
Semi-Markovian jump neural networks (semi-MJNNs); sojourn-time-dependent transition rates (TRs); stochastic synchronization; time-varying delay
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Funding
- BK21 Plus Program (Development of Advanced Smart Mechatronics Systems) through the Ministry of Education (MOE, Korea) [22A20130000136]
- National Natural Science Foundation of China [61503091]
- Australian Research Council [DP170103305]
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Continuous-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.
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