Referenced Items are not available for this document.
This paper presents a recurrent neural-network model for solving a special class of general variational inequalities (GVIs), which includes classical VIs as special cases. It is proved that the proposed neural network (NN) for solving this class of GVIs can be globally convergent, globally asymptotically stable, and globally exponentially stable under different conditions. The proposed NN can be viewed as a modified version of the general projection NN existing in the literature. Several numerical examples are provided to demonstrate the effectiveness and performance of the proposed NN
Published in:
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
(Volume:37
,
Issue:
3
)
Date of Publication: June 2007
- Page(s):
- 528 - 539
- ISSN :
- 1083-4419
- INSPEC Accession Number:
- 9456001
- Digital Object Identifier :
- 10.1109/TSMCB.2006.886166
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 15 May 2007
- Issue Date :
- June 2007
- Sponsored by :
- IEEE Systems, Man, and Cybernetics Society
- PubMed ID :
- 17550109
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