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利用稳态和动态校准信息,辨识了基于Hammerstein模型的热线式空气质量流量(MAF)传感器模型结构。模型辨识采用两步法,在用多项式逼近静态非线性特性的基础上,动态线性环节分别选取ARX模型、输出误差(OE)模型和Box-Jenkins(BJ)模型,采用交叉准则法进行参数估计和阶次选择,模型的残差分析和用验证数据对模型交叉检验的结果表明,最终输出误差(FOE)准则和最终预报误差(FPE)准则选择的阶次一致,基于预测误差法的3阶OE和BJ模型均可用于热线式MAF传感器Hammerstein模型动态线性环节的建模。
Based on the steady-state and dynamic calibration information, a hot-wire air mass flow (MAF) sensor model structure based on the Hammerstein model was identified. The model identification is based on the two-step method. Based on the polynomial approximation of the static nonlinearity, the ARX model, the output error (OE) model and the Box-Jenkins (BJ) The results of order selection, model residual analysis and cross-validation of the model with validation data show that the order of the final output error (FOE) criterion and the final forecast error criterion (FPE) criterion are the same. The third order OE And BJ models can all be used to model the dynamic linearity of the Hammerstein model of hotline MAF sensors.