English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/30537
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:
Title

Variable selection procedures and efficient suboptimal mask search algorithms in fuzzy inductive reasoning

AuthorsMirats-Tur, Josep M. ; Cellier, François E.; Huber Garrido, Rafael
KeywordsVariable selection
Behavioural modelling
Inductive modelling
Fuzzy inductive reasoning
Suboptimal mask search
Hill-climbing
Control theory
Issue Date2002
PublisherTaylor & Francis
CitationInternational Journal of General Systems 31(5): 469-498 (2002)
AbstractThis paper describes two new suboptimal mask search algorithms for Fuzzy inductive reasoning (FIR), a technique for modelling dynamic systems from observations of their input/output behaviour. Inductive modelling is by its very nature an optimisation problem. Modelling large-scale systems in this fashion involves solving a high-dimensional optimisation problem, a task that invariably carries a high computational cost. Suboptimal search algorithms are therefore important. One of the two proposed algorithms is a new variant of a directed hill-climbing method. The other algorithm is a statistical technique based on spectral coherence functions. The utility of the two techniques is demonstrated by means of an industrial example. A garbage incinerator process is inductively modelled from observations of 20 variable trajectories. Both suboptimal search algorithms lead to similarly good models. Each of the algorithms carries a computational cost that is in the order of a few percent of the cost of solving the complete optimisation problem. Both algorithms can also be used to filter out variables of lesser importance, i.e. they can be used as variable selection tools.
Publisher version (URL)http://dx.doi.org/10.1080/0308107021000042471
URIhttp://hdl.handle.net/10261/30537
DOI10.1080/0308107021000042471
ISSN0308-1079
Appears in Collections:(IRII) Artículos
Files in This Item:
File Description SizeFormat 
doc1.pdf399,51 kBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.