Solving future equation systems using integral-type error function and using twice ZNN formula with disturbances suppressed |
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| Authors: | Yang Shi Yunong Zhang |
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| Institution: | 1. School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510006, PR China;2. Research Institute of Sun Yat-sen University in Shenzhen, Shenzhen 518057, PR China;3. Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Guangzhou 510006, PR China |
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| Abstract: | In this paper, for solving future equation systems, two novel discrete-time advanced zeroing neural network models are proposed, analyzed and investigated. First of all, by using integral-type error function and twice zeroing neural network (or termed, Zhang neural network) formula, as the preliminaries and bases of future problems solving, two continuous-time advanced zeroing neural network models are presented for solving continuous time-variant equation systems. Secondly, a one-step-ahead numerical differentiation rule termed 5-instant discretization formula is presented for the first-order derivative approximation with higher computational precision. By exploiting the presented 5-instant discretization formula to discretize the continuous-time advanced zeroing neural network models, two novel discrete-time advanced zeroing neural network models are proposed. Theoretical analyses on the convergence and precision of the discrete-time advanced zeroing neural network models are proposed. In addition, in the presence of disturbance, the proposed discrete-time advanced zeroing neural network models still possess excellent performance. Comparative numerical experimental results further substantiate the efficacy and superiority of the proposed discrete-time advanced zeroing neural network models for solving the future equation systems. |
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| Keywords: | Corresponding author at: School of Information Science and Technology Sun Yat-sen University Guangzhou 510006 PR China |
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