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Noise effect in an improved conjugate gradient algorithm to invert particle size distribution and the algorithm amendment

机译:改进的共轭梯度算法中噪声效应反演粒度分布及其算法修正

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摘要

In general, model-independent algorithms are sensitive to noise during laser particle size measurement. An improved conjugate gradient algorithm (ICGA) that can be used to invert particle size distribution (PSD) from diffraction data is presented. By use of the ICGA to invert simulated data with multiplicative or additive noise, we determined that additive noise is the main factor that induces distorted results. Thus the ICGA is amended by introduction of an iteration step-adjusting parameter and is used experimentally on simulated data and some samples. The experimental results show that the sensitivity of the ICGA to noise is reduced and the inverted results are in accord with the real PSD.
机译:通常,与模型无关的算法对激光粒度测量期间的噪声敏感。提出了一种改进的共轭梯度算法(ICGA),该算法可用于从衍射数据中反转粒度分布(PSD)。通过使用ICGA对具有乘法或加性噪声的模拟数据进行反演,我们确定了加性噪声是导致失真结果的主要因素。因此,通过引入迭代步进调整参数来修改ICGA,并将ICGA用于模拟数据和一些样本的实验中。实验结果表明,ICGA对噪声的敏感性降低了,反演结果与实际PSD一致。

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