A survey on projection neural networks and their applications

被引:32
|
作者
Jin, Long [1 ,2 ]
Li, Shuai [3 ]
Hu, Bin [1 ]
Liu, Mei [1 ,2 ]
机构
[1] Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou, Gansu, Peoples R China
[2] Minist Educ, Key Lab Ind Internet Things & Networked Control, Chongqing, Peoples R China
[3] Hong Kong Polytech Univ, Dept Comp, Hung Hom, Kowloon, Hong Kong, Peoples R China
基金
湖南省自然科学基金; 中国国家自然科学基金;
关键词
Projection neural network; Linear programming; Quadratic programming; Redundant robot manipulators; Distributed control; Winner-take-all; Recurrent neural networks; Robust stability; MODEL-PREDICTIVE CONTROL; AUTONOMOUS UNDERWATER VEHICLES; QUADRATIC-PROGRAMMING PROBLEMS; TAKE-ALL NETWORKS; REPETITIVE MOTION; REDUNDANT MANIPULATORS; OBSTACLE-AVOIDANCE; NONLINEAR MODEL; VARIATIONAL-INEQUALITIES; OPTIMIZATION PROBLEMS;
D O I
10.1016/j.asoc.2019.01.002
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Constrained optimization problems arise in numerous scientific and engineering applications, and many papers on the online solution of constrained optimization problems using projection neural networks have been published in the literature. The purpose of this paper is to provide a comprehensive review of the research on projection neural networks for solving various constrained optimizations as well as their applications. Since convergence and stability are important for projection neural networks, theoretical results of projection neural networks are reviewed in detail. In addition, various applications of projection neural networks, e.g., the motion generation of redundant robot manipulators, coordination control of multiple robots with limited communications, generation of winner-take-all strategy, model predictive control and WSN localizations, are discussed and compared. Concluding remarks and future directions of projection neural networks as well as their applications are provided. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码:533 / 544
页数:12
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