Extended dissipativity synchronization for Markovian jump recurrent neural networks via memory sampled-data control and its application to circuit theory
- Authors
- Anbuvithya, R.; Sri, S. Dheepika; Vadivel, R.; Hammachukiattikul, P.; Park, Choonkil; Nallappan, Gunasekaran
- Issue Date
- Apr-2022
- Publisher
- SEMNAN UNIV
- Keywords
- Extended Dissipativity; Markovian Jump Recurrent Neural Networks; Memory sampled - data control; Synchronization
- Citation
- INTERNATIONAL JOURNAL OF NONLINEAR ANALYSIS AND APPLICATIONS, v.13, no.1, pp.2801 - 2820
- Indexed
- SCOPUS
- Journal Title
- INTERNATIONAL JOURNAL OF NONLINEAR ANALYSIS AND APPLICATIONS
- Volume
- 13
- Number
- 1
- Start Page
- 2801
- End Page
- 2820
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/138926
- DOI
- 10.22075/ijnaa.2021.25114.2919
- ISSN
- 2008-6822
- Abstract
- The problem of synchronization with extended dissipativity for Markovian Jump Recurrent Neural Networks (MJRNNs) is investigated. For MJRNNs, a new memory sampled - data extended dissipative control approach is suggested here. Some sufficient conditions in terms of Linear Matrix Inequalities (LMIs) are acquired by suitably establishing a relevant Lyapunov - Krasovskii functional (LKF), wherein the master and the slave system of MJRNNs are quadratically stable. At last, a nu-merical section is provided, along with one of the applications in circuit theory that clearly illustrates the efficacy of the proposed method's performance.
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