Stochastic exponential synchronization for delayed neural networks with semi-Markovian switchings: Saturated heterogeneous sampling communication |
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| Affiliation: | 1. College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China;2. College of Artificial Intelligence, Southwest University, Chongqing 400715, China;3. School of Computer Science and Technology, Tianjin Polytechnic University, Tianjin 300387, China;4. School of Computer and Information Science, Southwest University, Chongqing 400715, China;5. Brain-inspired Computing & Intelligent Control of Chongqing Key Lab, Chongqing 400715, China;6. National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology, Chongqing 400715, China;7. Chongqing Brain Science Collaborative Innovation Center, Chongqing 400715, China;1. School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650500, China;2. College of Mathematics and Statistics, Guangxi Normal University, Guilin 541006, China;1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China;2. College of Information Science and Technology, Donghua University, Shanghai 201620, China;3. Freshman Education Department, Yangtze University, Jing Zhou, Hubei 434023, China;4. School of Mathematics and Statistics, Anyang Normal University, Anyang 455000, China;5. Department of Mathematics and Finance, Yunyang Teachers’ College, Shiyan, Hubei 442000, China |
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| Abstract: | By implementing heterogeneous sampling communication mechanism, this article addresses the exponential synchronization issue of drive–response chaotic neural networks (CNNs) with interval time-varying delays by simultaneously taking into account the semi-Markovian switchings and saturating actuators. More specifically, a semi-Markovian jumping model whose transition rates (TRs) are not constant but depends on the sojourn time (ST) is introduced to characterize the stochastic changing among the interaction of CNNs, which makes the NNs model under consideration more suitable for some actual circumstances. More particularly, we assume that the sampling intervals are heterogeneous and time-varying, which may be more practical in real-life applications than homogeneous sampling policy. Additionally, by introducing some new terms, one novel time-dependent Lyapunov–Krasovskii function (LKF) is ingeniously constructed, which can fully capture the characteristic information of heterogeneous sampling pattern. Benefitting from the introduced relaxed free-weighting matrices (FWM) and resorting to the formed LKF, some sampling-interval-dependent sufficient conditions for controller design of the resulting semi-MJNNs error system are established and expressed by linear matrix inequalities (LMIs). These LMIs-based constraints can be effectively checked by utilizing the available software packages. Therein, the developed synchronization criteria dependent on both the lower and upper bounds of sampling periods, and the available information about the actual sampling pattern is fully considered. Ultimately, two numerical examples are provided to demonstrate the feasibility and practicability of our theoretical findings. |
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| Keywords: | Heterogeneous sampling communication mechanism (HSCM) Semi-Markovian switchings (SMSs) Actuator saturations Chaotic neural networks (CNNs) Linear matrix inequalities (LMIs) |
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