基于RBF神经网络的压气机特性仿真  

Compressor characteristic simulation based on RBF neural network

作　　者：彭靖波[1] 谢寿生[1] 

机构地区：[1]空军工程大学工程学院,陕西西安710038

出　　处：《推进技术》2006年第1期30-32,共3页Journal of Propulsion Technology

摘　　要：为克服传统仿真方法误差较大的问题,提出了一种基于RBF(多变量插值的径向基函数)神经网络的压气机特性仿真方法。利用RBF神经网络能逼近任意非线性系统的特点,对压气机特性进行了拟合。试验结果表明,此方法具有精度高,收敛速度快等优点,可广泛运用于发动机数值仿真及控制模拟等领域。To overcome the weakness of traditional method, a simulation method of compressor in aeroengine based on RBF neural network was developed. According to the character of RBF neural network that it can approach any nonlinear system, compressor characteristic was simulated. The result implies that this method has high precision and fast convergence pace and it can be applied to many fields such as aeroengine numeral simulation, control simulation and so on.

关 键 词：RBF神经网络 压气机特性 仿真 非线性系统 

分 类 号：V233.7[航空宇航科学与技术—航空宇航推进理论与工程]