Coexisting behaviors of a fraction-order novel hyperbolic-type memristor Hopfield neuron network based on three neurons

DOI10.1142/S0217979220503026zbMath1454.92004OpenAlexW3110269420WikidataQ115523913 ScholiaQ115523913MaRDI QIDQ5151980

Zongli Yang, Lianghui Ding, Yongbin Hu, Jun Luo, Xiangyu Shan, Da-wei Ding

Publication date: 18 February 2021

Published in: International Journal of Modern Physics B (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0217979220503026

zbMATH Keywords

memristorcoexisting behaviorsfraction-order systemHopfield neuron networkhyperbolic-type

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Qualitative investigation and simulation of ordinary differential equation models (34C60) Fractional ordinary differential equations (34A08)

Cites Work

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• Global exponential stability of high-order Hopfield-type neural networks with S-type distributed time delays
• Stability analysis of uncertain fuzzy Hopfield neural networks with time delays
• A study of network dynamics
• Fractional diffusion equations by the Kansa method
• Fractional differential equations. An introduction to fractional derivatives, fractional differential equations, to methods of their solution and some of their applications
• Nonlinear dynamics and chaos in fractional-order neural networks
• Synchronization-based parameter estimation of fractional-order neural networks
• Advances in Fractional Calculus
• Neurons with graded response have collective computational properties like those of two-state neurons.

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