Model inversion using fuzzy neural network with boosting of the solution
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In: International Journal of Pure and Applied Mathematics, Vol. 80, No. 2, 2012, p. 261-270.
Research output: Journal contributions › Journal articles › Research › peer-review
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TY - JOUR
T1 - Model inversion using fuzzy neural network with boosting of the solution
AU - Mercorelli, P.
AU - Nentwig, Mirko
N1 - Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - Neural networks are a very effective and popular tool for modeling. The inversion of a neural network makes possible the use of these networks in control problem schemes. This paper presents an inversion strategy based upon a feed-forward trained local linear model tree. The local linear model tree is realized through a fuzzy neural network. AMS Subject Classification: 92B20, 03B52, 90B15
AB - Neural networks are a very effective and popular tool for modeling. The inversion of a neural network makes possible the use of these networks in control problem schemes. This paper presents an inversion strategy based upon a feed-forward trained local linear model tree. The local linear model tree is realized through a fuzzy neural network. AMS Subject Classification: 92B20, 03B52, 90B15
KW - Engineering
KW - Regelungstechnik
KW - Antriebstechnik
KW - Fuzzy logic
KW - Networks model
KW - Neural networks
UR - http://www.scopus.com/inward/record.url?scp=84867836477&partnerID=8YFLogxK
M3 - Journal articles
AN - SCOPUS:84867836477
VL - 80
SP - 261
EP - 270
JO - International Journal of Pure and Applied Mathematics
JF - International Journal of Pure and Applied Mathematics
SN - 1311-8080
IS - 2
ER -