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

被引：70

作者：

Ahmadi, Mohammad Hossien ^{[1
]}

Aghaj, Saman Sorouri Ghare ^{[2
]}

Nazeri, Alireza ^{[2
]}

机构：

[1] KN Toosi Univ, Fac Mech Engn, Tehran, Iran

[2] Islamic Azad Univ, Dept Ind Engn, Sci & Res Branch, Hormozgan, Iran

来源：

NEURAL COMPUTING & APPLICATIONS | 2013年 / 22卷 / 06期

关键词：

Solar dish; Stirling heat engine; Artificial neural network; Particle swarm optimization; Genetic algorithm; Hybrid;

D O I：

10.1007/s00521-012-0880-y

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1141 / 1150

页数：10

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