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

• Published in: Neural computing & applications Vol. 22; no. 6; pp. 1141 - 1150
• Main Authors: Ahmadi, Mohammad Hossien, Sorouri Ghare Aghaj, Saman, Nazeri, Alireza
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01.05.2013; Springer
• Subjects: Applied sciences; Artificial Intelligence; Calculus of variations and optimal control; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer science; control theory; systems; Data Mining and Knowledge Discovery; Exact sciences and technology; Image Processing and Computer Vision; Inference from stochastic processes; time series analysis; Learning and adaptive systems; Mathematical analysis; Mathematics; Numerical analysis; Numerical analysis. Scientific computation; Numerical methods in mathematical programming, optimization and calculus of variations; Numerical methods in optimization and calculus of variations; Original Article; Probability and statistics; Probability and Statistics in Computer Science; Sciences and techniques of general use; Statistics; Solar dish; Hybrid; Stirling heat engine; Artificial neural network; Genetic algorithm; Particle swarm optimization; Neural computation; Experimental data; Optimization method; Prediction; Neural network; Optimization
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-012-0880-y

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.