 Reachable set estimation of delayed Markovian jump neural networks via variables-augmented-based free-weighting-matrices methodXu-Kang Chang and Yong He Applied Mathematics and Computation, 2024, vol. 478, issue C Abstract: The problem of the reachable set estimation for delayed Markovian jump neural networks is investigated in this article. Firstly, to take full account of the information of state variables and time delay, an augmented delay-product-type Lyapunov-Krasovskii functional (LKF) is established for obtaining an accurate reachable set described by an ellipsoid. Some single and quadratic integral states are introduced into the LKF as augmented variables to reduce the conservatism of the reachable set result. However, the introduction of the integral states causes the LKF derivative to generate higher-order terms of the time-varying delay. Then, the variables-augmented-based free-weighting-matrices method is introduced to solve this problem. The method makes the LKF derivative turn into a linear function and gives more freedom to derive an improved result. Finally, a more accurate reachable set is obtained, whose merits are shown by a numerical example. Keywords: Markovian jump neural networks; Reachable set estimation; Time-varying delay; Lyapunov-Krasovskii functional; Free-weighting matrices (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:478:y:2024:i:c:s0096300324002984 DOI: 10.1016/j.amc.2024.128837 Access Statistics for this article Applied Mathematics and Computation is currently edited by Theodore Simos More articles in Applied Mathematics and Computation from Elsevier |
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