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| Format: | Preprint |
| Published: |
2025
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| Online Access: | https://arxiv.org/abs/2507.06063 |
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| _version_ | 1866918126315110400 |
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| author | Yamada, Yuhei |
| author_facet | Yamada, Yuhei |
| contents | Physical reservoir computing is an innovative idea for using physical phenomena as computational resources. Recent research has revealed that information processing techniques can improve the performance, but for practical applications, it is equally important to study the level of performance with a simple design that is easy to construct experimentally. We focus on a reservoir composed of independent hysteretic systems as a model suitable for the practical implementation of physical reservoir computing. In this paper, we discuss the appropriate design of this reservoir, its performance, and its limitations. This research will serve as a practical guideline for constructing hysteresis-based reservoirs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06063 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Practical design and performance of physical reservoir computing using hysteresis Yamada, Yuhei Emerging Technologies Neural and Evolutionary Computing Physical reservoir computing is an innovative idea for using physical phenomena as computational resources. Recent research has revealed that information processing techniques can improve the performance, but for practical applications, it is equally important to study the level of performance with a simple design that is easy to construct experimentally. We focus on a reservoir composed of independent hysteretic systems as a model suitable for the practical implementation of physical reservoir computing. In this paper, we discuss the appropriate design of this reservoir, its performance, and its limitations. This research will serve as a practical guideline for constructing hysteresis-based reservoirs. |
| title | Practical design and performance of physical reservoir computing using hysteresis |
| topic | Emerging Technologies Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2507.06063 |