A Review on Zeroing Neural Networks
Fuente:
arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866916818261639168 |
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| author | Jiang, Chengze Gui, Jie Jin, Long Li, Shuai |
| author_facet | Jiang, Chengze Gui, Jie Jin, Long Li, Shuai |
| contents | Zeroing neural networks (ZNNs) have demonstrated outstanding performance on time-varying optimization and control problems. Nonetheless, few studies are committed to illustrating the relationship among different ZNNs and the derivation of them. Therefore, reviewing the advances for a systematical understanding of this field is desirable. This paper provides a survey of ZNNs' progress regarding implementing methods, analysis theory, and practical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_00387 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Review on Zeroing Neural Networks Jiang, Chengze Gui, Jie Jin, Long Li, Shuai Neural and Evolutionary Computing Zeroing neural networks (ZNNs) have demonstrated outstanding performance on time-varying optimization and control problems. Nonetheless, few studies are committed to illustrating the relationship among different ZNNs and the derivation of them. Therefore, reviewing the advances for a systematical understanding of this field is desirable. This paper provides a survey of ZNNs' progress regarding implementing methods, analysis theory, and practical applications. |
| title | A Review on Zeroing Neural Networks |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2507.00387 |