INGAR   05399
INSTITUTO DE DESARROLLO Y DISEÑO
Unidad Ejecutora - UE
capítulos de libros
Título:
Estimation of industrial latex quality using a General Regression Neural Network
Autor/es:
GEORGINA STEGMAYER,; JORGE VEGA,; CHIOTTI, OMAR; LUIS GUGLIOTTA,
Libro:
IFIP International Federation for Information Processing, AI in Theory and Practice II
Editorial:
Springer
Referencias:
Año: 2008; p. 255 - 264
Resumen:
This paper presents a neural-based model for estimating the particle size
distribution (PSD) of a polymer latex, which is an important physical characteristic
that determines some end-use properties of the material (e.g., when it is used as an
adhesive, a coating, or an ink). The PSD of a dilute latex is estimated from combined
DLS (dynamic light scattering) and ELS (elastic light scattering) measurements,
taken at several angles. To this effect, a neural network approach is used as a tool
for solving the involved inverse problem. The method utilizes a general regression
neural network (GRNN), which is able to estimate the PSD on the basis of both
the average intensity of the scattered light in the ELS experiments, and the average
diameters calculated from the DLS measurements. The GRNN was trained with a
large set of measurements simulated from typical asymmetric PSDs, represented by
unimodal normal-logarithmic distributions of variable geometric mean diameters
and variances. The proposed approach was successfully evaluated on the basis of
both simulated and experimental examples