Acta Photonica Sinica, Volume. 49, Issue 10, 1029001(2020)
Research on Weighted Bayesian Inversion Algorithm with Non-negative Least Squares Constraint
In the multi-angle dynamic light scattering for nanoparticle size analysis, the weighted Bayesian inversion algorithm is proved to have a good anti-noise capability. However, it suffers from initial value sensitivity and long time-consuming. This paper presents a method of non-negative least squares constrained weighted Bayesian inversion algorithm, in which the results of the non-negative least squares method are used as the prior value as well as the optimization range of the median diameter and peak width of the weighted Bayesian algorithm. The simulated and experimental results demonstrate that this method can improve significantly the convergence and the anti-noise performance of the unimodal particle system. When there is a big noise, the convergence speed is increased by more than 8 times and the distribution error is guaranteed to be within 0.070 9.
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Yi-zhuo LIANG, Ling LIU, Li PENG, Jian QIU, Kai-qing LUO, Dong-mei LIU, Peng HAN. Research on Weighted Bayesian Inversion Algorithm with Non-negative Least Squares Constraint[J]. Acta Photonica Sinica, 2020, 49(10): 1029001
Category: Scattering
Received: Jun. 30, 2020
Accepted: Aug. 28, 2020
Published Online: Mar. 10, 2021
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