State estimation for semi-Markovian switching CVNNs with quantization effects and linear fractional uncertainties |
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| Authors: | Qiang Li Jinling Liang Weiqiang Gong |
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| Institution: | 1. School of Mathematics, Southeast University, Nanjing 210096, PR China;2. School of Applied Mathematics, Nanjing University of Finance and Economics, Nanjing 210023, PR China;1. School of Mathematics, Southeast University, Nanjing 210096, China;2. School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650500, China;3. Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea;4. Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, Gyongsan 38541, Republic of Korea;1. School of Science, Anhui Agricultural University, Hefei 230036, PR China;2. School of Mathematics, Southeast University, Nanjing 210096, PR China;1. School of Information Engineering, Fuyang Normal University, Fuyang 236041, PR China;2. School of Science, Hubei University for Nationalities, Enshi, Hubei 445000, PR China;3. School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, Sichuan 610036, PR China;4. School of Mathematical Sciences, Chongqing Normal University, Chongqing 401331, PR China;5. Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, Kyongsan 38541, Republic of Korea;6. Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia;1. College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China;2. Institute of Complexity Science, Qingdao University, Qingdao 266071, China;3. College of Automation Engineering, Qingdao University of Technology, Qingdao 266555, China |
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| Abstract: | This paper is concerned with the robust state estimation problem for semi-Markovian switching complex-valued neural networks with quantization effects (QEs). The uncertain parameters are described by the linear fractional uncertainties (LFUs). To enhance the channel utilization and save the communication resources, the measured output is quantized before transmission by a logarithmic quantizer. The purpose of the problem under consideration is to design a full-order state estimator to estimate the complex-valued neuron states. Based on the Lyapunov stability theory, stochastic analysis method, and some improved integral inequalities, sufficient conditions are first derived to guarantee the estimation error system to be globally asymptotically stable in the mean square. Then, the desired state estimator can be directly designed after solving a set of matrix inequalities, which is robust against the LFUs and the QEs. In the end of the paper, one numerical example is provided to illustrate the feasibility and effectiveness of the proposed estimation design scheme. |
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