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Processes
Volume 13
Issue 9
10.3390/pr13092802
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Elman Network Classifier Based on Hyperactivity Rat Swarm Optimizer and Its Applications for AlSi10Mg Process Classification

by
Rui Ni
1,
Hanning Chen
2,
Xiaodan Liang
2,
Maowei He
2,
Yelin Xia
3 and
Liling Sun
2,*
1
School of Mechanical Engineering, Tiangong University, Tianjin 300387, China
2
School of Computer Science and Technology, Tiangong University, Tianjin 300387, China
3
School of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin 300457, China
*
Author to whom correspondence should be addressed.
Processes 2025, 13(9), 2802; https://doi.org/10.3390/pr13092802
Submission received: 16 July 2025 / Revised: 17 August 2025 / Accepted: 28 August 2025 / Published: 1 September 2025
(This article belongs to the Section Manufacturing Processes and Systems)
Versions Notes

Abstract

Classification prediction technology, which utilizes labeled data for training to enable autonomous decision, has emerged as a pivotal tool across numerous fields. The Elman neural network (ENN) exhibits potential in tackling nonlinear problems. However, its computational process faces inherent limitations in escaping local optimum and experiencing a slow convergence rate. To improve these shortcomings, an ENN classifier based on Hyperactivity Rat Swarm Optimizer (HRSO), named HRSO-ENNC, is proposed in this paper. Initially, HRSO is divided into two phases, search and mutation, by means of a nonlinear adaptive parameter. Subsequently, five search actions are introduced to enhance the global exploratory and local exploitative capabilities of HRSO. Furthermore, a stochastic roaming strategy is employed, which significantly improves the ability to jump out of local positions. Ultimately, the integration of HRSO and ENN enables the substitution of the original gradient descent method, thereby optimizing the neural connection weights and thresholds. The experiment results demonstrate that the accuracy and stability of HRSO-ENNC have been effectively verified through comparisons with other algorithm classifiers on benchmark functions, classification datasets and an AlSi10Mg process classification problem.
Keywords: classification prediction; Rat Swarm Optimizer; Elman neural network; hyperactivity search actions; stochastic roaming strategy classification prediction; Rat Swarm Optimizer; Elman neural network; hyperactivity search actions; stochastic roaming strategy

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MDPI and ACS Style

Ni, R.; Chen, H.; Liang, X.; He, M.; Xia, Y.; Sun, L. Elman Network Classifier Based on Hyperactivity Rat Swarm Optimizer and Its Applications for AlSi10Mg Process Classification. Processes 2025, 13, 2802. https://doi.org/10.3390/pr13092802

AMA Style

Ni R, Chen H, Liang X, He M, Xia Y, Sun L. Elman Network Classifier Based on Hyperactivity Rat Swarm Optimizer and Its Applications for AlSi10Mg Process Classification. Processes. 2025; 13(9):2802. https://doi.org/10.3390/pr13092802

Chicago/Turabian Style

Ni, Rui, Hanning Chen, Xiaodan Liang, Maowei He, Yelin Xia, and Liling Sun. 2025. "Elman Network Classifier Based on Hyperactivity Rat Swarm Optimizer and Its Applications for AlSi10Mg Process Classification" Processes 13, no. 9: 2802. https://doi.org/10.3390/pr13092802

APA Style

Ni, R., Chen, H., Liang, X., He, M., Xia, Y., & Sun, L. (2025). Elman Network Classifier Based on Hyperactivity Rat Swarm Optimizer and Its Applications for AlSi10Mg Process Classification. Processes, 13(9), 2802. https://doi.org/10.3390/pr13092802

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