基于遗传神经网络的太阳能集热器仿真研究  

Research on Solar Collector Simulation Based on Genetic-BP Algorithm

作　　者：李满峰[1] 李素萍[1] 范波[1] 

机构地区：[1]华北水利水电学院,河南省郑州市450011

出　　处：《中国电机工程学报》2012年第5期126-130,共5页Proceedings of the CSEE

摘　　要：提出神经网络和数学模型相结合的方法建立太阳能集热器神经网络模型,并采用遗传算法对太阳能集热器神经网络模型进行优化,用以提高仿真模型的预测精度。在热管式真空管太阳能集热器实验数据的基础上,构建反向传输神经网络模型和遗传算法-反向传播神经网络模型,应用数学模型、反向传输神经网络模型和遗传算法-反向传播神经网络模型进行仿真计算,结果表明遗传算法-反向传播神经网络模型的计算精度高于数学模型和反向传输神经网络模型。同时该建模方法应用于太阳能集热器仿真,既提高了仿真模型的计算精度,又提高了仿真模型的通用性。The solar collector neural network model was built based on the artificial neural network and the theoretical model.Genetic algorithm was used to optimize forecast accuracy of the neural network mode.On the basis of experimental data of solar collectors,the genetic algorithm-back propagation（GA-BP） neural network model was set up.The mathematical model,the BP neural network and the GA-BP neural network model of solar collectors were used for simulation calculation.The simulation result shows that the GA-BP neural network model is more accurate than the mathematical model and the BP neural network.The application of the GA-BP neural network can improve the accuracy and versatility of the simulation model.

关 键 词：遗传算法 反向传输神经网络 太阳能集热器 预测精度 数学模型 

分 类 号：TK515[动力工程及工程热物理—热能工程]