Input-to-state stability for dynamical neural networks with time-varying delays

DOI10.1155/2012/372324zbMath1261.34059OpenAlexW2108192929WikidataQ58696444 ScholiaQ58696444MaRDI QIDQ1938179

Publication date: 4 February 2013

Published in: Abstract and Applied Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2012/372324

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Stability theory of functional-differential equations (34K20)

Related Items (1)

Exponential input-to-state stability of stochastic Cohen-Grossberg neural networks with mixed delays

Cites Work

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\(\mathcal L_{2}-\mathcal L_{\infty }\) nonlinear system identification via recurrent neural networks
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A characterization of integral input-to-state stability
Smooth stabilization implies coprime factorization
Global stability analysis of neural networks with multiple time varying delays
Neurons with graded response have collective computational properties like those of two-state neurons.
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