Global exponential stability of discrete-time neural networks for constrained quadratic optimization
نویسندگان
چکیده
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. c © 2003 Elsevier B.V. All rights reserved.
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ورودعنوان ژورنال:
- Neurocomputing
دوره 56 شماره
صفحات -
تاریخ انتشار 2004