文献类型：期刊文献

英文题名：Non-convex activated zeroing neural network model for solving time-varying nonlinear minimization problems with finite-time convergence

作者：Si, Yang[1];Wang, Difeng[2];Chou, Yao[3];Fu, Dongyang[1]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Minist Nat Resources, Inst Oceanog 2, State Key Lab Satellite Ocean Environm Dynam, Hangzhou 310012, Peoples R China;[3]Brigham Young Univ, Dept Elect & Comp Engn, Provo, UT 84602 USA

年份：2023

卷号：274

外文期刊名：KNOWLEDGE-BASED SYSTEMS

收录：SCI-EXPANDED(收录号：WOS:001015302200001)、、EI(收录号：20232314185460)、Scopus(收录号：2-s2.0-85160798042)、WOS

基金：This research was funded by the National Natural Science Foundation of China, with contract No. 41476157, the National Key R & D Program of China, with contract No. 2018YFB0505005, the Key Projects of the Guangdong Education Department with contract No. 2019KZDXM019 and the Guangdong Graduate Academic Forum Project with contract No. 230420003.

语种：英文

外文关键词：Time-varying nonlinear minimization; Finite-time convergent non-convex zeroing; neural network (FT-NCZNN); Motion generation

外文摘要：Zeroing neural network (ZNN) model is a powerful tool for solving time-varying nonlinear minimization problems. This study presents some limitations of existing ZNN models, mainly related to convex and unsaturation constraints. To this end, a projection method is introduced for constructing nonconvex activation functions, whereby a finite-time convergent non-convex zeroing neural network (FT-NCZNN) model is proposed based on this method. The model has a faster convergence rate than the original zeroing neural network (OZNN) model. Rigorous theoretical analyses are provided to verify the convergence of the model as well as the upper boundary on convergence time. Subsequently, through a series of simulations, the superior performance of the FT-NCZNN model under different noise conditions is demonstrated. Finally, an engineering application on motion generation is introduced for a double-linked manipulator to further illustrate the validity and feasibility of the FT-NCZNN model. & COPY; 2023 Elsevier B.V. All rights reserved.