根据卡尔曼滤波新息过程的统计特点，将新息过程部分反馈到DR方程以抑制DR的误差发散。
Innovations of FKF are partly fed back to DR equations to restrain the accumulation of error according to the characteristic of innovations.
以新息序列的平均平方和为评价函数优化扫描光谱的峰位，消除扫描过程中可能产生的波长定位误差，从而保证滤波结果的准确性，并使实际检出限显著改善。
The whiteness of the innovation sequence for an optimal filter was explored to be the criterion for the correction of the wavelength positioning errors which may occur in spectral scans.
仿真研究给出了基于新息灰理论的工业实例建模过程和故障预测结果，并与BP神经网络方法的计算结果进行比较，验证了新息灰预测方法的有效性与实用性。
The modeling procedure of emulation based on FIGPM is presented, and the prediction result is compared with BP neural networks. It is proved that IGPM has good practicability and validity.
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