Noise-Robust Projection Rule for Rotor and Matrix-Valued Hopfield Neural Networks

Kobayashi, Masaki

doi:10.1109/tnnls.2020.3028091

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Cited by 3 publications

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“…1. Hyperbolic-valued Hopfield neural networks (HHNNs; Kuroe, Tanigawa, & Iima, 2011;Kobayashi, 2013Kobayashi, , 2016Kobayashi, , 2018cKobayashi, , 2019Kobayashi, , 2020. 2.…”

Section: Introductionmentioning

confidence: 99%

Hyperbolic-Valued Hopfield Neural Networks in Synchronous Mode

Kobayashi

2020

Neural Computation

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For most multistate Hopfield neural networks, the stability conditions in asynchronous mode are known, whereas those in synchronous mode are not. If they were to converge in synchronous mode, recall would be accelerated by parallel processing. Complex-valued Hopfield neural networks (CHNNs) with a projection rule do not converge in synchronous mode. In this work, we provide stability conditions for hyperbolic Hopfield neural networks (HHNNs) in synchronous mode instead of CHNNs. HHNNs provide better noise tolerance than CHNNs. In addition, the stability conditions are applied to the projection rule, and HHNNs with a projection rule converge in synchronous mode. By computer simulations, we find that the projection rule for HHNNs in synchronous mode maintains a high noise tolerance.

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“…1. Hyperbolic-valued Hopfield neural networks (HHNNs; Kuroe, Tanigawa, & Iima, 2011;Kobayashi, 2013Kobayashi, , 2016Kobayashi, , 2018cKobayashi, , 2019Kobayashi, , 2020. 2.…”

Section: Introductionmentioning

confidence: 99%