Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
FPGA-based implementation of an intelligent simulator for stand-alone photovoltaic system
Expert Systems with Applications: An International Journal
Genetically evolved radial basis function network based prediction of drill flank wear
Engineering Applications of Artificial Intelligence
Hybrid optimization scheme for radial basis function neural network
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Combining in situ flow cytometry and artificial neural networks for aquatic systems monitoring
Expert Systems with Applications: An International Journal
Training neural networks with harmony search algorithms for classification problems
Engineering Applications of Artificial Intelligence
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Radial basis function neural networks (RBFNs) can be applied to model the I-V characteristics and maximum power points (MPPs) of photovoltaic (PV) panels. The key issue for training an RBFN lies in determining the number of radial basis functions (RBFs) in the hidden layer. This paper presents a genetic algorithms-based RBFN training scheme to search for the optimal number of RBFs using only the input samples of a PV panel. The performance of the trained RBFN is comparable with that of the conventional model and the training algorithm is computationally efficient. The trained RBFNs have been applied to predict MPPs of two different practical PV panels. The results obtained are accurate enough for applying the models to control the PV systems for tracking the optimal power points.