A One-Layer Recurrent Neural Network for Pseudoconvex Optimization Subject to Linear Equality Constraints

被引：96
|
作者：
Guo, Zhishan [1 ]
Liu, Qingshan [2 ]
Wang, Jun [3 ]
机构：
[1] Univ N Carolina, Dept Comp Sci, Chapel Hill, NC 27599 USA
[2] Southeast Univ, Sch Automat, Nanjing 210096, Peoples R China
[3] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Hong Kong, Hong Kong, Peoples R China
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS | 2011年 / 22卷 / 12期
关键词：
Global convergence; linear equality constraints; pseudoconvex optimization; recurrent neural networks; LIMITING ACTIVATION FUNCTION; GROSS ERROR-DETECTION; VARIATIONAL-INEQUALITIES; 7; KINDS; DESIGN;
D O I：
10.1109/TNN.2011.2169682
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a one-layer recurrent neural network is presented for solving pseudoconvex optimization problems subject to linear equality constraints. The global convergence of the neural network can be guaranteed even though the objective function is pseudoconvex. The finite-time state convergence to the feasible region defined by the equality constraints is also proved. In addition, global exponential convergence is proved when the objective function is strongly pseudoconvex on the feasible region. Simulation results on illustrative examples and application on chemical process data reconciliation are provided to demonstrate the effectiveness and characteristics of the neural network.
引用
收藏
页码：1892 / 1900
页数：9
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