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Neural Computing and Applications

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01.05.2013 | Original Article

Prediction of power in solar stirling heat engine by using neural network based on hybrid genetic algorithm and particle swarm optimization

verfasst von: Mohammad Hossien Ahmadi, Saman Sorouri Ghare Aghaj, Alireza Nazeri

Erschienen in: Neural Computing and Applications | Ausgabe 6/2013

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Abstract

In this paper, the model based on a feed-forward artificial neural network optimized by particle swarm optimization (HGAPSO) to estimate the power of the solar stirling heat engine is proposed. Particle swarm optimization is used to decide the initial weights of the neural network. The HGAPSO-ANN model is applied to predict the power of the solar stirling heat engine which data set reported in literature of china. The performance of the HGAPSO-ANN model is compared with experimental output data. The results demonstrate the effectiveness of the HGAPSO-ANN model.

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Titel: Prediction of power in solar stirling heat engine by using neural network based on hybrid genetic algorithm and particle swarm optimization
verfasst von: Mohammad Hossien Ahmadi
Saman Sorouri Ghare Aghaj
Alireza Nazeri
Publikationsdatum: 01.05.2013
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 6/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-0880-y

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