Contraction Analysis of Hopfield Neural Networks with Hebbian Learning-Reference-Cited by-同舟云学术

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Contraction Analysis of Hopfield Neural Networks with Hebbian Learning

Published:2022-12-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 61st Conference on Decision and Control (CDC)
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Author:

Centorrino Veronica¹,Bullo Francesco²,Russo Giovanni³

Affiliation:

1. University of Naples Federico II,Scuola Superiore Meridionale,Italy

2. University of California,Center for Control, Dynamical Systems, and Computation,Department of Mechanical Engineering,Santa Barbara,USA

3. University of Salerno,Department of Information and Electric Engineering and Applied Mathematics,Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9992315/9992317/09993009.pdf?arnumber=9993009

Reference33 articles.

1. Contraction Analysis of Hopfield Neural Networks with Hebbian Learning

2. Non-Euclidean Contraction Theory for Robust Nonlinear Stability

3. Non-Euclidean Contractivity of Recurrent Neural Networks

4. The measure of a matrix as a tool to analyze computer algorithms for circuit analysis

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1. Neuromimetic Dynamic Networks with Hebbian Learning;2024 American Control Conference (ACC);2024-07-10

2. Modeling and contractivity of neural-synaptic networks with Hebbian learning;Automatica;2024-06

3. Positive Competitive Networks for Sparse Reconstruction;Neural Computation;2024-05-10

4. Cluster Synchronization as a Mechanism of Free Recall in Working Memory Networks;IEEE Open Journal of Control Systems;2023

5. Contraction Analysis of Hopfield Neural Networks with Hebbian Learning;2022 IEEE 61st Conference on Decision and Control (CDC);2022-12-06

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