Paper Info
Title
Evolutionary Hybrid Configuration Applied to a Polymerization Process Modelling
Abstract
A modelling procedure based on hybrid configuration composed of artificial neural networks, differential evolution and clonal selection algorithms is developed and applied in this work. The neural network represents the model of the system, while the differential evolution and clonal selection algorithms perform a simultaneous topological and parametric optimization of the model. The results indicated that the combination of the two optimizers produces better results compared with each of them working separately. As case study, styrene polymerization, a complex process which is difficult to model when taking into consideration all the internal interactions, was chosen. Neural networks, designed in an optimal form, proved to be adequate tools for modelling this system.
Year
DOI
Venue
2015
10.1007/978-3-319-19222-2_20
ADVANCES IN COMPUTATIONAL INTELLIGENCE, PT II
Keywords
Field
DocType
Neural network,Differential evolution,Clonal selection,Styrene polymerization
Parametric optimization,Pattern recognition,Computer science,Process modeling,Algorithm,Polymerization,Differential evolution,Artificial intelligence,Artificial neural network,Clonal selection
Conference
Volume
ISSN
Citations 
9095
0302-9743
0
PageRank 
References 
Authors
0.34
22
3
Name
Order
Citations
PageRank
Silvia Curteanu1636.26
Elena-Niculina Dragoi2333.18
Vlad Dafinescu3120.87