Integrated Photonic Reservoir Computing with Tunable Memory Capacity
We propose a novel integrated reservoir computing scheme with dynamically adjustable short-term memory capacity, ranging from 0-20. Our scheme can efficiently complete NARMA time series prediction tasks while meeting different memory capacity requirements.
Authors
- Quan Chen (ORCID: https://orcid.org/0000-0001-7539-8728)
- Kun Xu (ORCID: https://orcid.org/0000-0002-1663-9998)
- Yihang Lai
- Tian Zhang (ORCID: https://orcid.org/0000-0001-8914-3531)
- Jian Dai (ORCID: https://orcid.org/0000-0002-0486-0628)
- Zhiwei Yang (ORCID: https://orcid.org/0000-0002-6379-056X)
- Zhenzhen Cai (ORCID: https://orcid.org/0000-0003-4831-8897)
Institutions
- Beijing University of Posts and Telecommunications (CN)
Publication Details
- Published
- 2025-06-29
- DOI
- https://doi.org/10.23919/oecc/psc62146.2025.11109445
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities