On the global output convergence of a class of recurrent neural networks with time-varying inputs

DOI10.1016/J.NEUNET.2004.10.005zbMath1071.68537OpenAlexW1964856397WikidataQ51518798 ScholiaQ51518798MaRDI QIDQ557639

Derong Liu, Sanqing Hu

Publication date: 30 June 2005

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2004.10.005

zbMATH Keywords

OptimizationLipschitz continuityGlobal output convergenceLyapunov diagonal stabilityRecurrent neural networksTime-varying input

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (5)

Global output convergence of recurrent neural networks with distributed delays ⋮ Global output convergence of Cohen-Grossberg neural networks with both time-varying and distributed delays ⋮ On Exponential Convergence Conditions of an Extended Projection Neural Network ⋮ Long-Range Out-of-Sample Properties of Autoregressive Neural Networks ⋮ Design and analysis of a novel chaotic diagonal recurrent neural network

Cites Work

• Qualitative analysis of large scale dynamical systems
• Delay structure conditions for identifiability of closed loop systems
• A deterministic annealing neural network for convex programming
• New sufficient conditions for absolute stability of neural networks
• Analysis and design of a recurrent neural network for linear programming
• Global exponential stability of a class of neural circuits
• New conditions for global stability of neural networks with application to linear and quadratic programming problems
• Neural networks and physical systems with emergent collective computational abilities.
• Neurons with graded response have collective computational properties like those of two-state neurons.

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