Learnable-order fractional recurrent networks: Structured identification of memory in non-stationary sequences.
- DOI
- 10.1016/j.neunet.2026.109537
- Published
- 2026 Aug 20
- Container
- Neural networks : the official journal of the International Neural Network Society
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.109537,
title = {Learnable-order fractional recurrent networks: Structured identification of memory in non-stationary sequences.},
author = {Rajchakit G and Tajudeen MM and Banu KA and Lim CP and Tatar NE and Huang T},
year = {2026},
journal = {Neural networks : the official journal of the International Neural Network Society},
doi = {10.1016/j.neunet.2026.109537},
url = {https://doi.org/10.1016/j.neunet.2026.109537}
}RIS
TY - JOUR TI - Learnable-order fractional recurrent networks: Structured identification of memory in non-stationary sequences. AU - Rajchakit G AU - Tajudeen MM AU - Banu KA AU - Lim CP AU - Tatar NE AU - Huang T PY - 2026 JO - Neural networks : the official journal of the International Neural Network Society DO - 10.1016/j.neunet.2026.109537 UR - https://doi.org/10.1016/j.neunet.2026.109537 ER -
APA
G, R., MM, T., KA, B., CP, L., NE, T., & T, H. (2026). Learnable-order fractional recurrent networks: Structured identification of memory in non-stationary sequences.. Neural networks : the official journal of the International Neural Network Society. https://doi.org/10.1016/j.neunet.2026.109537
Source records
- pubmed · retrieved 2026-09-25T23:54:58.874Z