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

Autor:	Mohammad Hossien Ahmadi, Alireza Nazeri, Saman Sorouri Ghare Aghaj
Rok vydání:	2012
Předmět:	Stirling engine Artificial neural network Computer science Computer Science::Neural and Evolutionary Computation Particle swarm optimization Power (physics) law.invention Data set Artificial Intelligence Control theory law Genetic algorithm Multi-swarm optimization Software Simulation Heat engine
Zdroj:	Neural Computing and Applications. 22:1141-1150
ISSN:	1433-3058 0941-0643
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::27a0b6f8d19f260f01709a0c12bc3bda https://doi.org/10.1007/s00521-012-0880-y Zobrazit plný text záznamu Full text from SpringerLink