该方法是将拾振器的输出信号作为FIR横向滤波器的输入，用自适应rls算法对FIR横向滤波器系数进行辨识而最终获得可以恢复拾振器原始输入信号的反卷积滤波器。
The coefficients of the FIR transversal filter was identified by using adaptive RLS algorithm while the output signal of the vibration pick-up was input into the FIR transversal filter.
反卷积滤波器的结构等效于输出观测器和一个线性映射，该线性映射反映了未知输入与输出估计误差之间的内在联系。
The deconvolution filter is made up of an output observer and a linear mapping, where the latter reflects the internal connection between the unknown input signals and the output estimate error.
该流量模型的特征是其求和函数及其输入输出函数的边界函数均受限于一个最小加卷积函数。
The model fully illustrated the stochastic characteristic of WMN with the property that the input-output relation and sum functions of this model were bounded by a min-plus convolution function.
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