A new method to study global exponential stability of inertial neural networks with multiple time-varying transmission delays
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DOI: 10.1016/j.matcom.2023.04.008
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- Huang, Chuangxia & Su, Renli & Cao, Jinde & Xiao, Songlin, 2020. "Asymptotically stable high-order neutral cellular neural networks with proportional delays and D operators," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 171(C), pages 127-135.
- Yaning Yu & Ziye Zhang, 2022. "State Estimation for Complex-Valued Inertial Neural Networks with Multiple Time Delays," Mathematics, MDPI, vol. 10(10), pages 1-14, May.
- Chen, Yonghui & Zhang, Xian & Xue, Yu, 2022. "Global exponential synchronization of high-order quaternion Hopfield neural networks with unbounded distributed delays and time-varying discrete delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 173-189.
- Wang, Junlan & Wang, Xin & Wang, Yantao & Zhang, Xian, 2021. "Non-reduced order method to global h-stability criteria for proportional delay high-order inertial neural networks," Applied Mathematics and Computation, Elsevier, vol. 407(C).
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Cited by:
- Juan Yu & Kailong Xiong & Cheng Hu, 2024. "Synchronization Analysis for Quaternion-Valued Delayed Neural Networks with Impulse and Inertia via a Direct Technique," Mathematics, MDPI, vol. 12(7), pages 1-22, March.
- Arunagirinathan, S. & Lee, T.H., 2024. "Generalized delay-dependent reciprocally convex inequality on stability for neural networks with time-varying delay," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 217(C), pages 109-120.
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Keywords
Inertial neural networks; Global exponential stability; Parameterized method; Multiple time-varying transmission delays;All these keywords.
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