基于混沌PSO_Elman网络的航空发动机基线挖掘  

Aero-engine baseline mining based on chaotic PSO_Elman network

作　　者：瞿红春[1] 林文斌 许旺山 郭龙飞 QU Hongchun;LIN Wenbin;XU Wangshan;GUO Longfei(College of Aeronautical Engineering,CAUC,Tianjin 300300,China)

机构地区：[1]中国民航大学航空工程学院,天津300300

出　　处：《中国民航大学学报》2020年第1期19-23,53,共6页Journal of Civil Aviation University of China

基　　金：中央高校基本科研业务费专项(ZXH2010D019);中国民航大学科研基金项目(05KY08M)。

摘　　要：为提高发动机基线的拟合精度,提出经混沌粒子群优化的Elman神经网络模型。利用混沌算法改进粒子群算法(PSO)的位置公式,以解决局部最优问题。利用非线性递减函数改进PSO粒子的速度公式,以解决收敛精度较低的问题。将该模型用于基线拟合,并与传统的误差反向传播网络(BP)、Elman网络、支持向量机(SVM)等模型的拟合误差进行对比。结果表明:在训练数据、测试数据、训练次数均相同的情况下,混沌PSO_Elman模型的拟合精度高于其他传统模型;当训练样本减少时,其拟合精度依然高于传统模型,证明该模型具有更强的学习能力。In order to improve the fitting precision of engine baseline,an Elman neural network model optimized by chaotic particle swarm is proposed.The position formula of PSO is improved by chaos algorithm to solve the local optimization problem.A nonlinear decreasing function is proposed to improve the PSO particle velocity formula to solve the problem of low convergence accuracy.The model is applied in baseline fitting and the fitting errors are compared with those of traditional BP network,Elman network and SVM.Results show that the fitting accuracy of chaotic PSO_Elman model is higher than that of other traditional models with the same training and testing data and the same training times.When the training samples get fewer,the fitting accuracy is still higher than the traditional models,proving the stronger learning ability of the model.

关 键 词：航空发动机 基线挖掘 混沌 粒子群算法 ELMAN神经网络 

分 类 号：V239[航空宇航科学与技术—航空宇航推进理论与工程]