A discrete-time recurrent neural network with global exponential stability for constrained linear variational inequalities

In this paper, a discrete-time recurrent neural network with global exponential stability is proposed for solving constrained linear variational inequalities. Compared with the existing neural networks for linear variational inequalities, the proposed neural network in this paper has lower model com...

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Bibliographic Details
Published inProceedings of the 31st Chinese Control Conference pp. 3296 - 3301
Main Authors Liu Qingshan, Yang Wankou
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2012
Subjects
Control theory
Convergence
Discrete-time recurrent neural network
Globally exponentially stable
Linear variational inequalities
Optimization
Recurrent neural networks
Stability
Vectors
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ISBN1467325813
9781467325813
ISSN1934-1768

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Summary:In this paper, a discrete-time recurrent neural network with global exponential stability is proposed for solving constrained linear variational inequalities. Compared with the existing neural networks for linear variational inequalities, the proposed neural network in this paper has lower model complexity with only one-layer structure. The global exponential stability of the neural network can be guaranteed under some mild conditions. Simulation results show the performance and characteristics of the proposed neural network.
ISBN:1467325813
9781467325813
ISSN:1934-1768