Stability of memristor neural networks with delays operating in the flux-charge domain
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Publication:1661827
DOI10.1016/j.jfranklin.2018.04.011zbMath1395.93442OpenAlexW2796865093MaRDI QIDQ1661827
Mauro Forti, Mauro Di Marco, Luca Pancioni
Publication date: 17 August 2018
Published in: Journal of the Franklin Institute (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jfranklin.2018.04.011
Learning and adaptive systems in artificial intelligence (68T05) Nonlinear systems in control theory (93C10) Lyapunov and other classical stabilities (Lagrange, Poisson, (L^p, l^p), etc.) in control theory (93D05) Control/observation systems governed by ordinary differential equations (93C15)
Related Items (7)
Stabilizability of complex complex-valued memristive neural networks using non-fragile sampled-data control ⋮ Aperiodic switching event-triggered stabilization of continuous memristive neural networks with interval delays ⋮ Mixed \(H_\infty\)/passive exponential synchronization for delayed memristive neural networks with switching event-triggered control ⋮ Generalized state estimation criteria for additive delayed memristor neural networks including leakage delay effect-flux-charge domain applications in energy storage systems ⋮ A memristive conservative chaotic circuit consisting of a memristor and a capacitor ⋮ The amplitude, frequency and parameter space boosting in a memristor-meminductor-based circuit ⋮ New method to analyze the invariant manifolds of memristor circuits
Uses Software
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