摘要
离心泵内实际流动规律难以完全掌握,因此目前离心泵的设计仍然处在经验与理论半结合的程度,且预测准确度不理想。本文中提出了一种新的离心泵性能预测模型,采用遗传算法(GA)与BP神经网络相结合,对离心泵相关性能参数的优化设计方法进行研究。使用遗传算法优化神经网络的权值和阈值,然后用BP算法训练网络,避免了单独使用BP神经网络训练时易于陷入局部极小值的问题。引用具有代表性的一些离心泵性能相关数据作为新模型的训练样本,结果表明基于遗传算法来优化BP神经网络的参数,模型的收敛速度加快,训练精度较高。
It is difficult to understand the actual flow inside a centrifugal pump, whose design is still semi-empirical and semi-theoretical and whose prediction accuracy is not satisfactory. Thus, we establish a new model for predicting its performance. We combine the genetic algorithm with the BP neural network to conduct the optimal design of its performance parameters. We use the genetic algorithm to optimize the weighted values and threshold values of the BP neural network, and then use the BP algorithm to train the neural network, thus avoiding the local minimum values when the training is done with the BP neural network alone. We use some representative performance data of the centrifugal pump as training samples of the new model. The study results show that the BP neural network optimized with the genetic algorithm speeds up the convergence of the model and improves the training accuracy. Our method can serve as an effective theoretical basis for the optimal design of performance parameters of a centrifugal pump .
出处
《机械科学与技术》
CSCD
北大核心
2012年第8期1274-1279,共6页
Mechanical Science and Technology for Aerospace Engineering
基金
国家自然科学基金项目(51075345)
北京市科技计划项目(D101105046310002)
北京市农林科学院科技创新能力建设专项项目(KJCX201104010)资助
关键词
BP神经网络
遗传算法
离心泵性能
预测
BP neural network
genetic algorithm
performance prediction
centrifugal pump
