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Cited by 42Neural Networks and ApplicationsStochastic Gradient Optimization TechniquesMachine Learning and ELM

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Weak and strong convergence analysis of Elman neural networks via weight decay regularization

Zhou, Li, Fan, Qinwei, Huang, Xiaodi, Liu, Yan·2023·Optimization
DOI 10.1080/02331934.2022.2057852
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Abstract

In this paper, we propose a novel variant of the algorithm to improve the generalization performance for Elman neural networks (ENN). Here, the weight decay term, also called L2 regularization, which can effectively control the value of weights excessive growth, also over-fitting phenomenon can be effectively prevented. The main contribution of this work lies in that we have conducted a rigorous theoretical analysis of the proposed approach, i.e. the weak and strong convergence results are obtained. The comparison experiments to the problems of function approximation and classification on the real-world data have been performed to verify the theoretical results.

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Metadata

Authors
Zhou, L., Fan, Q., Huang, X., & Liu, Y.
Affiliations
  • Xi'an Polytechnic University (CN)
  • Charles Sturt University (AU)
  • Dalian Polytechnic University (CN)
Corresponding author
Fan, Qinwei
ORCID
Venue
Optimization
Publisher
Informa UK Limited
Published
2022-04-04
Volume
72
Issue
9
Pages
2287-2309
ISSN
  • 0233-1934 (ISSN-L)
  • 1029-4945
DOI
10.1080/02331934.2022.2057852
OpenAlex
W4226208947
Cited by
42
References
2
Citations by year
2026: 2 · 2025: 4 · 2024: 12 · 2023: 2 · 2022: 22
FWCI
3.29
Type
journal article
Language
en
Open access
closed
Locations
Optimization (publishedVersion)
Funders
  • Natural Science Basic Research Plan in Shaanxi Province of China (No.2021JM-446)
  • China Postdoctoral Science Foundation (No.2019-ZD-0128)
  • Natural Science Foundation Guidance Project of Liaoning Province (2019M652837)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
Keywords
Elman neural network, weight decay regularization, Tikhonov regularization, weak convergence, strong convergence, generalization performance, overfitting, function approximation, classification

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CoChat. (2026). Citation check: Weak and strong convergence analysis of Elman neural networks via weight decay regularization. https://app.cochat.ai/papers/10.1080/02331934.2022.2057852
The paper
Zhou, L., Fan, Q., Huang, X., & Liu, Y. (2023). Weak and strong convergence analysis of Elman neural networks via weight decay regularization. Optimization. https://doi.org/10.1080/02331934.2022.2057852