Novel discontinuous control for exponential synchronization of memristive recurrent neural networks with heterogeneous time-varying delays |
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| Authors: | Ruimei Zhang Deqiang Zeng Ju H Park Shouming Zhong Yongbin Yu |
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| Institution: | 1. School of Mathematics Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China;2. Data Recovery Key Laboratory of Sichuan Province, Numerical Simulation Key Laboratory of Sichuan Province, Neijiang Normal University, Neijiang, Sichuan 641100, PR China;3. Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, Kyongsan 38541, Republic of Korea;4. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, PR China |
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| Abstract: | This paper investigates the exponential synchronization problem of memristive recurrent neural networks (MRNNs) with heterogeneous time-varying delays (HTVDs). First, a novel discontinuous feedback control is designed, in which a tunable scalar is introduced. The tunable scalar makes the controller more flexible in reducing the upper bound of the control gain. Based on this control scheme, the double integral term can be successfully used to construct the LKF. Second, New method for tackling memristive synaptic weights and new estimation technique are presented. Third, based on the LKF and estimation technique, synchronization criterion is derived. In comparison with existing results, the established criterion is less conservatism thanks to the double integral term of the LKF. Finally, numerical simulations are presented to validate the effectiveness and advantages of the proposed results. |
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| Keywords: | Corresponding authors |
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