A recurrent neural network with exponential convergence for solving convex quadratic program and related linear piecewise equations

This paper presents a recurrent neural network for solving strict convex quadratic programming problems and related linear piecewise equations. Compared with the existing neural networks for quadratic program, the proposed neural network has a one-layer structure with a low model complexity. Moreove...

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Bibliographic Details
Published in:Neural networks 2004-09, Vol.17 (7), p.1003-1015
Main Authors: Xia, Youshen, Feng, Gang, Wang, Jun
Format: Article
Language:English
Subjects:
Applied sciences
Artificial Intelligence
Computer science
control theory
systems
Computer Simulation
Connectionism. Neural networks
Convex quadratic program
Exact sciences and technology
Exponential convergence
Finite-time convergence
Humans
Linear Models
Models, Neurological
Neural network
Neural Networks (Computer)
Piecewise equation
Software
Time Factors
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Summary:This paper presents a recurrent neural network for solving strict convex quadratic programming problems and related linear piecewise equations. Compared with the existing neural networks for quadratic program, the proposed neural network has a one-layer structure with a low model complexity. Moreover, the proposed neural network is shown to have a finite-time convergence and exponential convergence. Illustrative examples further show the good performance of the proposed neural network in real-time applications.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2004.05.006