NDLI: Solving Generally Constrained Generalized Linear Variational Inequalities Using the General Projection Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Xiaolin Hu Jun Wang
Copyright Year	1990
Abstract	Generalized linear variational inequality (GLVI) is an extension of the canonical linear variational inequality. In recent years, a recurrent neural network (NN) called general projection neural network (GPNN) was developed for solving GLVIs with simple bound (often box-type or sphere-type) constraints. The aim of this paper is twofold. First, some further stability results of the GPNN are presented. Second, the GPNN is extended for solving GLVIs with general linear equality and inequality constraints. A new design methodology for the GPNN is then proposed. Furthermore, in view of different types of constraints, approaches for reducing the number of neurons of the GPNN are discussed, which results in two specific GPNNs. Moreover, some distinct properties of the resulting GPNNs are also explored based on their particular structures. Numerical simulation results are provided to validate the results.
Sponsorship	IEEE Computational Intelligence Society
Starting Page	1697
Ending Page	1708
Page Count	12
File Size	811768
File Format	PDF
ISSN	10459227
Volume Number	18
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Recurrent neural networks Design methodology Asymptotic stability Neurons Numerical simulation Telecommunication traffic Traffic control Computational modeling Councils optimization Generalized linear variational inequality (GLVI) recurrent neural networks (NNs) global asymptotic stability global exponential stability
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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