基于改进BP神经网络的航空液压油软测量

Aircraft Hydraulic Fluids Soft Measurement Based on Improved BP Neural Network

BP神经网络越来越多地被应用于软测量建模中,与传统方法相比,BP神经网络进行信息处理可以减少数据的分析和建模工作,但也存在易于陷入局部最小值和初始权值随机选取的缺陷问题;为了解决传统BP神经网络存在的缺陷,文中在数据预处理过程中引入主成分分析法(PCA),在BP网络输入权值时引入遗传算法(GA),最终达到弥补BP神经网络缺陷的目的;详细介绍了改进算法的流程与步骤,将改进的BP神经网络应用于航空液压油的软测量,先是对航空液压油软测量参数进行分析,包括辅助变量的选择和数据预处理,然后进行基于改进型BP神经网络的建模与仿真实验;实验结果表明,基于改进BP神经网络的航空液压油软测量效果优于传统神经网络,具有更强的泛化能力,因此可进行更广泛的应用。

The BP neural network is increasingly used in the soft measurement modeling,compared with the traditional method,the BP neural network information processing can reduce the data analysis and modeling work,but there are also easy to fall into local minimum and the initial weights randomly selected defects.In order to solve the defects of traditional BP neural network,the thesis introduced in the process of data preprocessing,principal component analysis（PCA）,when the input of the BP network weights is introduced into the genetic algorithm（GA）,and finally achieve the purpose of make up for the BP neural network defects;Introduces in detail the process and steps of improved algorithm,the improved BP neural network was applied to the soft measurement of the aircraft hydraulic fluids detection,first analyze the aviation aircraft hydraulic fluids soft measurement parameters,including the selection of auxiliary variables and data preprocessing,and then based on the improved BP neural network modeling and simulation experiments.The experimental results show that the improved BP neural network model of the generalization ability is stronger,more widely,can achieve better measuring result,which make the BP neural Network can be used even more widely.