Abstract
This repository introduces the Long-Term Cognitive Network (LTCN) model for structured pattern classification problems. This recurrent neural network incorporates a quasi-nonlinear reasoning rule that allows controlling the amount of non-linearity in the reasoning mechanism. Furthermore, this neural classifier uses a recurrence-aware decision model that evades the issues posed by the unique fixed point while introducing a deterministic learning algorithm to compute the tunable parameters. The simulations show that this classifier obtains competitive results when compared to state-of-the-art white and black-box models.
| Original language | English |
|---|---|
| Publisher | Github.com |
| Media of output | Online |
| Publication status | Published - 2022 |
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Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification
Nápoles, G. (Corresponding author), Salgueiro, Y., Grau, I. & Espinosa, M. L., 1 Oct 2023, In: IEEE Transactions on Cybernetics. 53, 10, p. 6083-6094 12 p.Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile22 Link opens in a new tab Citations (Scopus)152 Downloads (Pure) -
On the Performance of the Nonsynaptic Backpropagation for Training Long-term Cognitive Networks
Nápoles, G., Grau, I., Concepcion, L. & Salgueiro, Y., 13 Oct 2021, Proceedings of the 11th International Conference of Pattern Recognition Systems (ICPRS 2021). Institute of Electrical and Electronics Engineers, 6 p. 9568988Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
2 Link opens in a new tab Citations (Scopus) -
Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification
Nápoles, G., Salgueiro, Y., Grau, I. & Espinosa, M. L., 2021, In: arXiv. 2021, 12 p., 2107.03423.Research output: Contribution to journal › Article › Academic
Open AccessFile27 Downloads (Pure)
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