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