Search Results - "generalised normal distribution optimisation algorithm"

A hybrid approach combining generalized normal distribution optimization algorithm and fuzzy C-means with Calinski-Harabasz index for clustering optimization

Authors: Moatasem Mahmood Ibrahim Omar Saber Qasim Talal Fadhil Hussein

Source: Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, Vol 15, Iss 3 (2025)

Subject Terms: feature selection, generalised normal distribution optimisation algorithm, fuzzy C-means clustering, data mining, Calinski-Harabasz index, Environmental engineering, TA170-171, Environmental sciences, GE1-350

File Description: electronic resource

Relation: https://ph.pollub.pl/index.php/iapgos/article/view/6921; https://doaj.org/toc/2083-0157; https://doaj.org/toc/2391-6761

Access URL: https://doaj.org/article/5077de9b3e1d401593f598fdc4be84c0

A hybrid approach combining generalized normal distribution optimization algorithm and fuzzy C-means with Calinski-Harabasz index for clustering optimizationHybrydowe podejście łączące uogólniony algorytm optymalizacji rozkładu normalnego i rozmyte C-średnie zewskaźnikiem Calińskiego-Harabaszado optymalizacji grupowania

Authors: Ibrahim, Moatasem Mahmood Qasim , Omar Saber Hussein, Talal Fadhil

Subject Terms: feature selection, generalised normal distribution optimisation algorithm, fuzzy C-means clustering, data mining, Calinski-Harabasz index, selekcja cech, uogólniony algorytm optymalizacji rozkładu normalnego, klastrowanie rozmytych C-średnich, eksploracja danych, wskaźnik Calińskiego-Harabasza

Access URL: http://yadda.icm.edu.pl/baztech/element/bwmeta1.element.baztech-7a5aabd5-67a3-4485-a61f-cd26b0c211aa

A HYBRID APPROACH COMBINING GENERALIZED NORMAL DISTRIBUTION OPTIMIZATION ALGORITHM AND FUZZY C-MEANS WITH CALINSKI-HARABASZ INDEX FOR CLUSTERING OPTIMIZATION.

Alternate Title: HYBRYDOWE PODEJŚCIE ŁĄCZĄCE UOGÓLNIONY ALGORYTM OPTYMALIZACJI ROZKŁADU NORMALNEGO I ROZMYTE C-ŚREDNIE ZE WSKAŹNIKIEM CALIŃSKIEGO-HARABASZA DO OPTYMALIZACJI GRUPOWANIA. (Polish)

Authors: Ibrahim, Moatasem Mahmood Qasim, Omar Saber Hussein, Talal Fadhil

Source: Informatics Control Measurement in Economy & Environment Protection / Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2025, Vol. 15 Issue 3, p10-14, 5p

Subject Terms: FUZZY clustering technique, CLUSTERING algorithms, OPTIMIZATION algorithms

A deep learning-based evolutionary model for short-term wind speed forecasting : A case study of the Lillgrund offshore wind farm

Authors: Neshat, Mehdi Nezhad, Meysam Majidi Abbasnejad, Ehsan et al.

Index Terms: Wind speed prediction, Short-term forecasting, Evolutionary algorithms, Generalised normal distribution optimisation, Deep learning models, Hybrid evolutionary deep learning method, Other Electrical Engineering, Electronic Engineering, Information Engineering, Annan elektroteknik och elektronik, Article in journal, info:eu-repo/semantics/article, text

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296386
Energy Conversion and Management, 0196-8904, 2021, 236

Wind field forecasting using a novel method based on convolutional neural networks and bidirectional LSTM.

Authors: Khalilabadi, Mohammad Reza

Source: Ships & Offshore Structures; Aug2024, Vol. 19 Issue 7, p892-900, 9p

Subject Terms: CONVOLUTIONAL neural networks, LONG short-term memory, WIND forecasting, WIND speed

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of the Wind Power and Photovoltaic Energy.

Authors: Dandan Xu Haijian Shao Xing Deng et al.

Source: Computer Modeling in Engineering & Sciences (CMES); 2022, Vol. 131 Issue 2, p567-597, 31p

Subject Terms: PHOTOVOLTAIC power generation, DEEP learning, WIND forecasting, WIND power, PHOTOVOLTAIC power systems, TECHNOLOGICAL forecasting, STATISTICAL learning

A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm

Authors: Neshat, M Nezhad, MM Abbasnejad, E et al.

Subject Terms: Electrical engineering, Electronics, sensors and digital hardware, Mechanical engineering, Chemical engineering

Relation: Energy Conversion and Management; Neshat, M; Nezhad, MM; Abbasnejad, E; Mirjalili, S; Tjernberg, LB; Astiaso Garcia, D; Alexander, B; Wagner, M, A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm, Energy Conversion and Management, 2021, 236, pp. 114002; https://hdl.handle.net/10072/404600

Availability: https://hdl.handle.net/10072/404600
https://doi.org/10.1016/j.enconman.2021.114002