Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0925-2312(03)00442-9
Title: Global exponential stability of discrete-time neural networks for constrained quadratic optimization
Authors: Tan, K.C. 
Tang, H.J.
Yi, Z.
Keywords: Discrete-time neural networks
Global exponential stability
Quadratic optimization
Issue Date: Jan-2004
Citation: Tan, K.C., Tang, H.J., Yi, Z. (2004-01). Global exponential stability of discrete-time neural networks for constrained quadratic optimization. Neurocomputing 56 (1-4) : 399-406. ScholarBank@NUS Repository. https://doi.org/10.1016/S0925-2312(03)00442-9
Abstract: A class of discrete-time recurrent neural networks for solving quadratic optimization problems over bound constraints is studied. The regularity and completeness of the network are discussed. The network is proven to be globally exponentially stable (GES) under some mild conditions. The analysis of GES extends the existing stability results for discrete-time recurrent networks. A simulation example is included to validate the theoretical results obtained in this letter. © 2003 Elsevier B.V. All rights reserved.
Source Title: Neurocomputing
URI: http://scholarbank.nus.edu.sg/handle/10635/56149
ISSN: 09252312
DOI: 10.1016/S0925-2312(03)00442-9
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