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Shi C, Hoi K, Vong S. Improved reciprocally convex inequality for stability analysis of neural networks with time-varying delay. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2023.01.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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Karnan A, Nagamani G. Synchronization of Uncertain Neural Networks with Additive Time-Varying Delays and General Activation Function. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-11074-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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State Estimation for Complex-Valued Inertial Neural Networks with Multiple Time Delays. MATHEMATICS 2022. [DOI: 10.3390/math10101725] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
In this paper, the problem of state estimation for complex-valued inertial neural networks with leakage, additive and distributed delays is considered. By means of the Lyapunov–Krasovskii functional method, the Jensen inequality, and the reciprocally convex approach, a delay-dependent criterion based on linear matrix inequalities (LMIs) is derived. At the same time, the network state is estimated by observing the output measurements to ensure the global asymptotic stability of the error system. Finally, two examples are given to verify the effectiveness of the proposed method.
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