Kong GQ, Guo LD. Stability and passivity analysis of delayed neural networks via an improved matrix-valued polynomial inequality.
Neural Netw 2024;
180:106637. [PMID:
39180908 DOI:
10.1016/j.neunet.2024.106637]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 07/08/2024] [Accepted: 08/14/2024] [Indexed: 08/27/2024]
Abstract
The stability and passivity of delayed neural networks are addressed in this paper. A novel Lyapunov-Krasovskii functional (LKF) without multiple integrals is constructed. By using an improved matrix-valued polynomial inequality (MVPI), the previous constraint involving skew-symmetric matrices within the MVPI is removed. Then, the stability and passivity criteria for delayed neural networks that are less conservative than the existing ones are proposed. Finally, three examples are employed to demonstrate the meliority and feasibility of the obtained results.
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