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Title:
Input-output modelling with decomposed neuro-fuzzy ARX model
Authors:
ID
Golob, Marjan
(
Author
)
ID
Tovornik, Boris
(
Author
)
Files:
http://dx.doi.org/10.1016/j.neucom.2007.02.011
Language:
English
Work type:
Unknown
Typology:
1.01 - Original Scientific Article
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
This paper presents a new neuro-fuzzy system based model, which is useful for the modelling of nonlinear dynamic systems. The new proposed model constitutes a soft computing method, namely, reasoning with a fuzzy inference system (FIS) and an optimisation by the neural-network learning algorithm. A structure, named the decomposed neuro-fuzzy ARX model is proposed. This structure is based on decomposition of the FIS. An evolution of a learning algorithm for the decomposed fuzzy model is suggested. A comparative study of dynamic system identification using conventional FIS models and the proposed neuro-fuzzy ARX model is presented for Box-Jenkins data set.
Keywords:
input-output modelling
,
fuzzy ARX model
,
neuro-fuzzy system
Year of publishing:
2008
PID:
20.500.12556/DKUM-27387
UDC:
007.52:681.5
ISSN on article:
0925-2312
COBISS.SI-ID:
11577622
NUK URN:
URN:SI:UM:DK:01C9OBKU
Publication date in DKUM:
01.06.2012
Views:
2871
Downloads:
102
Metadata:
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Record is a part of a journal
Title:
Neurocomputing
Shortened title:
Neurocomputing
Publisher:
Elsevier
ISSN:
0925-2312
COBISS.SI-ID:
172315
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