Chaos and asymptotical stability in discrete-time recurrent neural networks with generalized input-output function

DOI10.1007/BF02874421zbMath1006.65139OpenAlexW3088209944MaRDI QIDQ1609628

Jin-Liang Wang, Zhu-jun Jing, Luo-Nan Chen

Publication date: 15 August 2002

Published in: Science in China. Series A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02874421

zbMATH Keywords

snap-back repellerchaosneural networknumerical examplesasymptotic stabilitybifurcationdiscrete time

Mathematics Subject Classification ID

Numerical optimization and variational techniques (65K10) Stabilization of systems by feedback (93D15) Discrete-time control/observation systems (93C55) Input-output approaches in control theory (93D25) Asymptotic stability in control theory (93D20) Strange attractors, chaotic dynamics of systems with hyperbolic behavior (37D45) Numerical chaos (65P20) Computational methods for bifurcation problems in dynamical systems (37M20) Numerical bifurcation problems (65P30)

Related Items (2)

COMPLEX DYNAMICAL BEHAVIORS IN DISCRETE-TIME RECURRENT NEURAL NETWORKS WITH ASYMMETRIC CONNECTION MATRIX ⋮ Asymptotic stability of transiently chaotic neural networks

Cites Work

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• ``Neural computation of decisions in optimization problems
• Snap-back repellers imply chaos in \(\mathbb{R}^n\)
• Stability in asymmetric Hopfield nets with transmission delays
• Chaos and asymptotical stability in discrete-time neural networks
• Weakly connected neural networks
• Qualitative analysis and synthesis of a class of neural networks
• Global searching ability of chaotic neural networks
• 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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