建立准确的数学模型对于航空发动机的设计开发有十分重要的作用。航空发动机是一种非常复杂的非线性系统，基于外源输入下的非线性自回归滑动平均模型（NARMAX）可以很好地逼近任意非线性。为此，提出了采用改进的直交化最小二乘法并结合误差下降速率（ERR）算法，辨识航空发动机起动过程的NARMAX模型。结果表明，与最小二乘法辨识得到的结果相比，航空发动机起动过程的NARMAX模型更准确，收敛速度更快，能够满足发动机模型的精度与实时性要求，可以实时反映发动机性能。%Developing a mathematical model with high accuracy is of great importance to the design and development of aero-engine. The aero-engine is a complex nonlinear system. Nonlinear Auto Regressive Moving Average model with exogenous input(NARMAX)is able to model any kinds of nonlinearities. As a result,the aero-engine starting process was simulated by a NARMAX model utilizing an improved orthogonal least squares with error reduction ratio algorithm. According to the experimental results,the developed NARMAX model has a better accuracy and faster regression than the least squares method for identifying the aero-engine starting process. Therefore,the NARMAX model can meet the accuracy and real-time requirements,and thus capture the engine performance effectively in real-time.
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