Bifurcation analysis for a two-dimensional discrete-time Hopfield neural network with delays
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Publication:925380
DOI10.1155/2007/84260zbMath1145.39008OpenAlexW2087905634MaRDI QIDQ925380
Publication date: 3 June 2008
Published in: International Journal of Mathematics and Mathematical Sciences (Search for Journal in Brave)
Full work available at URL: https://eudml.org/doc/54495
stabilitybifurcationcenter manifold theorydelaysNeimark-Sacker bifurcation2D-neural networkdiscrete-time Hopfield neural network
Neural networks for/in biological studies, artificial life and related topics (92B20) Discrete version of topics in analysis (39A12) Bifurcation theory of functional-differential equations (34K18)
Cites Work
- Elements of applied bifurcation theory.
- Exponential stability of discrete-time Hopfield neural networks
- Stabilizing Hopfield neural networks via inhibitory self-connections
- Global asymptotic stability of Hopfield neural network involving distributed delays
- Periodic oscillation for discrete-time Hopfield neural networks
- Hopf bifurcation in numerical approximation of a \(n\)-dimension neural network model with multi-delays
- Stability, Bifurcation, and Multistability in a System of Two Coupled Neurons with Multiple Time Delays
- Introduction to Applied Nonlinear Dynamical Systems and Chaos
- 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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