Paper Info
Title
Modelling imprecise and scattered multidimensional data using granular data compression and multiple granularity modelling
Abstract
In this paper a systematic modelling approach is presented, involving two algorithmic procedures: a) a data pre-processing algorithm using granular computing and statistics and b) a granular neural-fuzzy ensemble network consisting of multiple granularity models. Both algorithmic procedures aim to reduce the data and modelling scatter often found in real industrial data. The study focuses on predicting the mechanical property of heat treated steel, in particular Charpy Toughness. This mechanical property yields high data scatter caused by unknown underlying fractural dynamics. The proposed methodology is shown to successfully model the process under investigation using a real industrial data set.
Year
DOI
Venue
2010
10.1109/GRC.2008.4664723
Hangzhou
Keywords
Field
DocType
artificial intelligence,data compression,fuzzy neural nets,data preprocessing algorithm,fractural dynamics,granular computing,granular data compression,granular neural-fuzzy ensemble network,heat treated steel,multiple granularity modelling,scattered multidimensional data
Charpy impact test,Fuzzy logic,Algorithm,Complex data type,Granular computing,Artificial intelligence,Toughness,Granularity,Data compression,Artificial neural network,Machine learning,Mathematics
Journal
Volume
Issue
ISBN
1
4
978-1-4244-2513-6
Citations 
PageRank 
References 
3
0.42
2
Authors
2
Name
Order
Citations
PageRank
George Panoutsos1577.59
Mahdi Mahfouf223533.17