New results on input-to-state convergence for recurrent neural networks with variable inputs

DOI10.1016/J.NONRWA.2007.03.019zbMath1154.34339OpenAlexW1992332809MaRDI QIDQ1003222

Yunxia Guo

Publication date: 27 February 2009

Published in: Nonlinear Analysis. Real World Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.nonrwa.2007.03.019

zbMATH Keywords

Lyapunov functionsrecurrent neural networksinput-to-state convergence

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Global stability of solutions to ordinary differential equations (34D23) Asymptotic properties of solutions to ordinary differential equations (34D05)

Related Items (1)

Stability analysis of linear time-varying time-delay systems by non-quadratic Lyapunov functions with indefinite derivatives

Cites Work

• Delay structure conditions for identifiability of closed loop systems
• Analysis and design of a recurrent neural network for linear programming
• New characterizations of input-to-state stability
• Smooth stabilization implies coprime factorization
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