Electricity consumption modeling by a chaotic convolutional radial basis function network-Reference-Cited by-同舟云学术

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Electricity consumption modeling by a chaotic convolutional radial basis function network

Published:2023-11-03 Issue: Volume: Page:
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Garcia Donaldo,Rubio José de Jesús,Sossa Humberto,Pacheco Jaime,Gutierrez Guadalupe Juliana,Aguilar-Ibañez Carlos

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s11227-023-05733-y.pdf

Reference33 articles.

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2. Montella R, Brizius A, Luccio DD, Porter C, Elliot J, Madduri R, Kelly D, Riccio A, Foster I (2020) Using the face-it portal and workflow engine for operational food quality prediction and assessment: an application to mussel farms monitoring in the bay of napoli, italy. Futur Gener Comput Syst 110:453–467

3. Nardo ED, Ciaramella A (2023) Tracking vision transformer with class and regression tokens. Inf Sci 619:276–287

4. Camastra F, Capone V, Ciaramella A, Riccio A, Staiano A (2022) Prediction of environmental missing data time series by support vector machine regression and correlation dimension estimation. Environ Model Softw 150:105343

5. Lucia GD, Lapegna M, Romano D (2023) Unlocking the potential of edge computing for hyperspectral image classification: an efficient low-energy strategy. Futur Gener Comput Syst 147:207–218

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