A novel finite-time complex-valued zeoring neural network for solving time-varying complex-valued Sylvester equation |
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| Institution: | 1. Department of Computer Science, Guindy Campus, University of Madras, Chennai600025, Tamil Nadu, India;2. Ramanujan Institute for Advanced Study in Mathematics, University of Madras, Chennai600005, Tamil Nadu, India;3. Department of Computer Science and Department of Network Systems and Information Technology, Guindy Campus, University of Madras, Chennai600025, Tamil Nadu, India;1. Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, PR China;2. Key Laboratory of System Control and Information Process, Ministry of Education, Shanghai 200240, PR China;3. Department of Mechnical Engineering, Politecnico di Milano, Milan 20156, Italy;1. College of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou, Hunan, 412007, China;2. Guangxi Power Grid Company Guilin Power Supply Bureau, Guilin,Guangxi, 541000, China;1. School of Macaronic Engineering and Automation, Shanghai University, Shanghai 200444, China;2. School of Mechanics and Engineering Science, Shanghai University, Shanghai 200072, China |
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| Abstract: | A novel finite-time complex-valued zeroing neural network (FTCVZNN) for solving time-varying Sylvester equation is proposed and investigated. Asymptotic stability analysis of this network is examined with any general activation function satisfying a condition or with an odd monotonically increasing activation function. So far, finite-time model studies have been investigated for the upper bound time of convergence using a linear activation function with design formula for the derivative of the error or with variations of sign-bi-power activation functions to zeroing neural networks. A function adaptive coefficient for sign-bi-power activation function (FA-CSBP) is introduced and examined for faster convergence. An upper bound on convergence time is derived with the two components in the function adaptive coefficients of sign-bi-power activation function. Numerical simulation results demonstrate that the FTCVZNN with function adaptive coefficient for sign-bi-power activation function is faster than applying a sign-bi-power activation function to the zeroing neural network (ZNN) and the other finite-time complex-valued models for the selected example problems. |
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