Title :
Estimation of the average chain length of polymers with neural classifiers
Author_Institution :
Dept. of Chem. Eng., Katholieke Univ., Leuven, Heverlee, Belgium
fDate :
27 Jun- 2 Jul 1994
Abstract :
This paper presents a method for estimating the average chain length of polymers based on neural networks. A simulation of a continuous solution polymerisation reactor, with varying setpoints, is used to train a self organising, winner-take-all, neural net which classifies the different average chain lengths, based on different input patterns. By means of a network parameter, the vigilance parameter, the accuracy-coarse or fine-of the classification intervals can be controlled. This parameter influences also the generalising capacity of the network. Pretrained networks are used to classify sets of untrained input patterns
Keywords :
chemical technology; pattern classification; polymerisation; process control; self-organising feature maps; continuous solution polymerisation reactor; neural classifiers; neural networks; polymer average chain length estimation; self-organising winner-take-all neural net; vigilance parameter; Chemical engineering; Chemical industry; Chemical processes; Equations; Expert systems; Inductors; Neural networks; Polymers; Production; Testing;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374820
