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Parameter extraction of photovoltaic models using a memory-based improved gorilla troops optimizer
Faculty
Computer Science
Year:
2022
Type of Publication:
ZU Hosted
Pages:
115134
Authors:
Karam mohamed goda
Staff Zu Site
Abstract In Staff Site
Journal:
Energy Conversion and Management Pergamon
Volume:
Keywords :
Parameter extraction , photovoltaic models using , memory-based
Abstract:
The parameter extraction of the PV model is a challenging issue owing to its multi-model and nonlinear characteristics. Moreover, these characteristics of the problem render the algorithms tackling it susceptible to being stuck in local opt
Author Related Publications
Karam mohamed goda, "An efficient teaching-learning-based optimization algorithm for parameters identification of photovoltaic models: Analysis and validations", Pergamon, 2021
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Karam mohamed goda, "BSMA: A novel metaheuristic algorithm for multi-dimensional knapsack problems: Method and comprehensive analysis", Pergamon, 2021
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Karam mohamed goda, "An Improved Binary Grey-Wolf Optimizer With Simulated Annealing for Feature Selection", IEEE, 2021
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Karam mohamed goda, "Evolutionary algorithm-based convolutional neural network for predicting heart diseases", Elsevier, 2021
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Karam mohamed goda, "An improved gaining-sharing knowledge algorithm for parameter extraction of photovoltaic models", Elsevier, 2021
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Department Related Publications
Ahmed Raafat Abass Mohamed Saliem, "BERT-CNN: A Deep Learning Model for Detecting Emotions from Text", Tech Science Press, 2021
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Ibrahiem Mahmoud Mohamed Elhenawy, "BERT-CNN: A Deep Learning Model for Detecting Emotions from Text", Tech Science Press, 2021
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Ahmed Raafat Abass Mohamed Saliem, "Using General Regression with Local Tuning for Learning Mixture Models from Incomplete Data Sets", ScienceDirect, 2010
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Ahmed Raafat Abass Mohamed Saliem, "On determining efficient finite mixture models with compact and essential components for clustering data", ScienceDirect, 2013
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Ahmed Raafat Abass Mohamed Saliem, "Unsupervised learning of mixture models based on swarm intelligence and neural networks with optimal completion using incomplete data", ScienceDirect, 2012
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