Bounded real lemma and <i>H</i><sub>∞</sub> control for BAM neural networks with unbounded time‐varying delays

Abstract This article investigates the exponential control issue of bidirectional associative memory neural network (BAMNN) with unbounded time‐varying delays. A bounded real lemma (BRL) is first established via a direct method, which is on the basis of the solutions of BAMNN. Second, based on the obtained BRL, the state feedback controller is designed to guarantee the global exponential stability of the resulting closed‐loop BAMNN with an performance index. Since no Lyapunov–Krasovskii functionals is constructed in the proposed method, the computation burden and complexity are reduced. Lastly, the effectiveness of the theoretical results is illustrated through two numerical examples.

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Publication Details

Journal
International Journal of Robust and Nonlinear Control
Published
2024-08-21
DOI
https://doi.org/10.1002/rnc.7606
Citations
3
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
1.05

Funders

Controls
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article

Bounded real lemma and H∞ control for BAM neural networks with unbounded time‐varying delays

Tingting Yu, Yu Xue, Zhuo Ren
3 citations
International Journal of Robust and Nonlinear Control
Neural Networks Stability and Synchronization
1.05
article

Bounded real lemma and H∞ control for BAM neural networks with unbounded time‐varying delays

Tingting Yu, Yu Xue, Zhuo Ren
article en
3 citations

Abstract

Abstract This article investigates the exponential control issue of bidirectional associative memory neural network (BAMNN) with unbounded time‐varying delays. A bounded real lemma (BRL) is first established via a direct method, which is on the basis of the solutions of BAMNN. Second, based on the obtained BRL, the state feedback controller is designed to guarantee the global exponential stability of the resulting closed‐loop BAMNN with an performance index. Since no Lyapunov–Krasovskii functionals is constructed in the proposed method, the computation burden and complexity are reduced. Lastly, the effectiveness of the theoretical results is illustrated through two numerical examples.

International Journal of Robust and Nonlinear ControlVol. 34(18)
Beijing Institute of Technology (CN), Heilongjiang University (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, Natural Science Foundation of Heilongjiang Province
Openalex Percentile: Top 22%
Neural Networks Stability and Synchronization
1.05
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