DocumentCode :
2725865
Title :
Particle Size Distribution from Combined Light Scattering Measurements. A Neural Network Approach for Solving the Inverse Problem
Author :
Stegmayer, G.S. ; Chiotti, O.A. ; Gugliotta, Luis M. ; Vega, Jorge R.
Author_Institution :
CIDISI, Santa Fe
fYear :
2006
fDate :
12-14 July 2006
Firstpage :
91
Lastpage :
95
Abstract :
A method is proposed for estimating the particle size distribution (PSD) of a latex with particle diameters in the sub-micrometer range, from combined elastic light scattering (ELS) and dynamic light scattering (DLS) measurements. The method is implemented through a general regression neural network (GRNN) that estimates the PSD from the ELS measurement carried out at several angles together with the average diameters of the PSD predicted by the DLS measurement at the same angles. The GRNN was trained with several measurements simulated on the basis of typical asymmetric PSDs. The ability of the trained GRNN was tested on the basis of two synthetic examples. The estimated PSDs are more accurate than those obtained through standard numerical techniques for `ill-conditioned´ inverse problems
Keywords :
inverse problems; light scattering; measurement by laser beam; neural nets; particle size measurement; physics computing; polymers; regression analysis; dynamic light scattering; elastic light scattering; general regression neural network; inverse problem; light scattering measurement; particle size distribution; Inverse problems; Iron; Light scattering; Mie scattering; Neural networks; Optical scattering; Particle measurements; Particle scattering; Size measurement; Software measurement; Dynamic Light Scattering; Elastic Light Scattering; Inverse Problems; Neural Network; Particle Size Distribution;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence for Measurement Systems and Applications, Proceedings of 2006 IEEE International Conference on
Conference_Location :
La Coruna
Print_ISBN :
1-4244-0244-1
Electronic_ISBN :
1-4244-0245-X
Type :
conf
DOI :
10.1109/CIMSA.2006.250762
Filename :
4016833
Link To Document :
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