Adaptive quantitative exponential synchronization in multiplex Cohen-Grossberg neural networks under deception attacks |
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| Institution: | 1. School of Computer Science and School of Cyberspace Science Xiangtan University, Xiangtan, 411105, PR China;2. School of Automation, Nanjing University of Science and Technology, Nanjing, 210094, PR China;3. School of Mathematics Science, Liaocheng University, Liaocheng, 252000, PR China;1. School of Mathematics and Statistics & FJKLMAA, Fujian Normal University, Fuzhou 350117, PR China;2. School of Mathematical Sciences, South China Normal University, Guangzhou 510631, PR China;1. College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;2. Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, Anhui Polytechnic University, Wuhu, 241000, China;3. School of Marine Engineering, Jimei University, China;4. NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211000, China;1. School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China;2. Hubei Key Laboratory of Optical Information and Pattern Recognition, Wuhan 430205, China;3. Hubei Province Key Laboratory of Systems Science in Metallurgical Process(Wuhan University of Science and Technology), Wuhan 430081, China |
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| Abstract: | In this article, a secure exponential synchronization problem is studied for multiplex Cohen-Grossberg neural networks under stochastic deception attacks. In order to resist the malicious attack from attackers modifying the data in transmission module under a certain probability, an attack resistant controller, which has the ability to automatically adjust its own parameters according to external attacks, is designed for each Cohen-Grossberg neural subnet. An exponential adaptive quantitative controlling algorithm is proposed to synchronize Cohen-Grossberg neural network state, and a sufficient criterion is established to realize the synchronization error tends to zero under malicious attacks. Moreover, synchronization mode we study is the synchronization among Cohen-Grossberg neural subnets in multiplex networks. An example is presented to testify the validity of proposed theoretical framework. |
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