Bumpless Transfer Control for Synchronization of Switched Neutral‐Type Neural Networks With a Reachable Set Strategy

ABSTRACT This investigation primarily centers on the reachable‐set‐based bumpless transfer control (BTC) for the synchronization of switched neutral‐type neural networks (SNNNs). In order to mitigate the conservatism inherent in the traditional state‐dependent switching strategies (SDSSs) and combin...

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Published inInternational journal of robust and nonlinear control Vol. 35; no. 6; pp. 2310 - 2323
Main Authors Li, Fang, Sang, Hong, Wang, Peng, Zhao, Ying, Ma, Yajing, Dimirovski, Georgi M.
Format Journal Article
LanguageEnglish
Published Hoboken, USA John Wiley & Sons, Inc 01.04.2025
Wiley Subscription Services, Inc
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ISSN1049-8923
1099-1239
DOI10.1002/rnc.7802

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Summary:ABSTRACT This investigation primarily centers on the reachable‐set‐based bumpless transfer control (BTC) for the synchronization of switched neutral‐type neural networks (SNNNs). In order to mitigate the conservatism inherent in the traditional state‐dependent switching strategies (SDSSs) and combined switching strategies (CSSs), an improved CSS leveraging the historical information of neuron states and neutral delay is developed. By constructing a time‐dependent multiple Lyapunov‐Krasovskii functional (TDMLF) technique, a less conservative criterion for reachable set estimation (RSE) is first established. In the subsequent, the established design framework is further employed by the BTC for the synchronization of SNNNs. The corresponding synchronization criterion is derived, which ensures that the resultant synchronization error influenced by bounded external inputs can be confined to an anticipated bounded set. Also, the underlying control bumps at switching instants during switching instants are effectively constrained to a specific level. Ultimately, the practicability and superiority of the proposed design framework are confirmed via two simulation examples.
Bibliography:This work was supported by the National Natural Science Foundation of China under Grants 62203080, 62273068, 52471374, the China Postdoctoral Science Foundation under Grant 2023M740467, the Natural Science Foundation of Liaoning Province under Grants 2023‐BS‐073, 2023‐MS‐252, and the Open Project Program of Center for Applied Mathematics of Fujian Province, Fuzhou University under Grants S20240903.
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ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.7802