Publication: Finite-Time Passivity-Based Stability Criteria for Delayed Discrete-Time Neural Networks via New Weighted Summation Inequalities
Issued Date
2019-01-01
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ISSN
21622388
2162237X
2162237X
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2-s2.0-85047638906
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Mahidol University
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SCOPUS
Bibliographic Citation
IEEE Transactions on Neural Networks and Learning Systems. Vol.30, No.1 (2019), 58-71
Suggested Citation
Ramasamy Saravanakumar, Sreten B. Stojanovic, Damnjan D. Radosavljevic, Choon Ki Ahn, Hamid Reza Karimi 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. Vol.30, No.1 (2019), 58-71. doi:10.1109/TNNLS.2018.2829149 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/50695
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Title
Finite-Time Passivity-Based Stability Criteria for Delayed Discrete-Time Neural Networks via New Weighted Summation Inequalities
Abstract
© 2012 IEEE. In 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.
