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Get Free AccessIn this paper, we study the problem of finite-time stability and passivity criteria for discrete-time neural networks (DNNs) with variable delays. The main objective is how to effectively evaluate the finite-time passivity conditions for NNs. To achieve this, some new weighted summation inequalities are proposed for application to a finite-sum term appearing in the forward difference of a novel Lyapunov-Krasovskii functional, which helps to ensure that the considered delayed DNN is passive. The derived passivity criteria are presented in terms of linear matrix inequalities. A numerical example is given to illustrate the effectiveness of the proposed results.
Ramasamy Saravanakumar, Sreten B. Stojanović, Damnjan D. Radosavljevic, Choon Ki Ahn, Hamid Reza Karimi (2018). Finite-Time Passivity-Based Stability Criteria for Delayed Discrete-Time Neural Networks via New Weighted Summation Inequalities. IEEE Transactions on Neural Networks and Learning Systems, 30(1), pp. 58-71, DOI: 10.1109/tnnls.2018.2829149.
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Type
Article
Year
2018
Authors
5
Datasets
0
Total Files
0
Language
English
Journal
IEEE Transactions on Neural Networks and Learning Systems
DOI
10.1109/tnnls.2018.2829149
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