文献类型：期刊文献

英文题名：Two neural dynamics approaches for computing system of time-varying nonlinear equations

作者：Xiao, Xiuchun[1,2];Fu, Dongyang[1,2];Wang, Guancheng[1,2];Liao, Shan[1,2];Qi, Yimeng[3];Huang, Haoen[1,2];Jin, Long[3]

机构：[1]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Shenzhen Inst, Shenzhen 518108, Peoples R China;[3]Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China

年份：2020

卷号：394

起止页码：84

外文期刊名：NEUROCOMPUTING

收录：SCI-EXPANDED(收录号：WOS:000531730600008)、、EI(收录号：20200808195405)、Scopus(收录号：2-s2.0-85079542824)、WOS

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61703189, in part by the International Science and Technology Cooperation Program of China under Grant 2017YFE0118900, in part by the Doctoral Initiating Project of Guangdong Ocean University under Grant E13428, in part by the Innovation and Strength Project of Guangdong Ocean University under Grant Q15090 and Grant 230419065, in part by the Research on Advanced Signal Processing Technology and Application of Department of Education of Guangdong Province (2017KCXTD015), in part by the Natural Science Foundation of Gansu Province, China, under Grant 18JR3RA264, in part by the Sichuan Science and Technology Program under Grant 19YYJC1656, in part by the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, under Grant 20190112, and in part by the Fundamental Research Funds for the Central Universities under Grant lzujbky2019-89.

语种：英文

外文关键词：Neural dynamics; Time-varying nonlinear equations; Gradient-based neural dynamics (GND); Zeroing neural dynamics (ZND)

外文摘要：The problem of solving time-varying nonlinear equations has received much attention in many fields of science and engineering. In this paper, firstly, considering that the classical gradient-based neural dynamics (GND) might result in nonnegligible residual error in handling time-varying nonlinear equations, an adaptive coefficient GND (AGND) model is constructed as an improvement. Besides, the secondly new designed model is the projected zeroing neural dynamics (PZND) to relieve the limitation on the available activation function, which can be of saturation and non-convexity different from that should be unbounded and convex described in the traditional zeroing neural dynamics (ZND) approach. Moreover, theoretical analyses on the AGND model and PZND model are provided to guarantee their effectiveness. Furthermore, computer simulations are conducted and analyzed to illustrate the efficacy and superiority of the two new neural dynamics models designed for online solving time-varying nonlinear equations. Finally, applications to robot manipulator motion generation and Lorenz system are provided to show the feasibility and practicability of the proposed approaches. (C) 2020 Elsevier B.V. All rights reserved.