DocumentCode
3402587
Title
Simulated annealing dynamic RPROP for training recurrent fuzzy systems
Author
Mastorocostas, P.A. ; Rekanos, I.T.
Author_Institution
Dept. of Informatics & Commun., Technol. Educ. Inst. of Serres
fYear
2005
fDate
25-25 May 2005
Firstpage
1086
Lastpage
1091
Abstract
An adaptive learning method for recurrent fuzzy systems is proposed. The method modifies the SARPROP algorithm, originally developed for static neural models, in order to be applied to dynamic models. A comparative analysis with dynamic RPROP and back propagation through time is given, indicating the enhanced learning capabilities of the proposed algorithm
Keywords
adaptive systems; backpropagation; fuzzy neural nets; fuzzy set theory; fuzzy systems; recurrent neural nets; simulated annealing; SARPROP algorithm; adaptive learning; back propagation; comparative analysis; dynamic RPROP; neural models; recurrent fuzzy system training; simulated annealing; Backpropagation algorithms; Communications technology; Convergence; Educational technology; Error correction; Fuzzy systems; Informatics; Learning systems; Neural networks; Simulated annealing;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on
Conference_Location
Reno, NV
Print_ISBN
0-7803-9159-4
Type
conf
DOI
10.1109/FUZZY.2005.1452546
Filename
1452546
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