Fuzzy function approximators with ellipsoidal regions

This paper discusses two types of fuzzy function approximators that dynamically generate fuzzy rules with ellipsoidal regions: a function approximator based on Takagi-Sugeno type model with the center-of-gravity defuzzification and a function approximator based on a radial basis function network. He...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society. - 1996. - 29(1999), 5 vom: 01., Seite 654-61
1. Verfasser: Abe, S (VerfasserIn)
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 1999
Zugriff auf das übergeordnete Werk:IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society
Schlagworte:Journal Article
LEADER 01000caa a22002652 4500
001 NLM177402628
003 DE-627
005 20250209044403.0
007 cr uuu---uuuuu
008 231223s1999 xx |||||o 00| ||eng c
024 7 |a 10.1109/3477.790450  |2 doi 
028 5 2 |a pubmed25n0591.xml 
035 |a (DE-627)NLM177402628 
035 |a (NLM)18252344 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Abe, S  |e verfasserin  |4 aut 
245 1 0 |a Fuzzy function approximators with ellipsoidal regions 
264 1 |c 1999 
336 |a Text  |b txt  |2 rdacontent 
337 |a ƒaComputermedien  |b c  |2 rdamedia 
338 |a ƒa Online-Ressource  |b cr  |2 rdacarrier 
500 |a Date Completed 02.10.2012 
500 |a Date Revised 06.02.2008 
500 |a published: Print 
500 |a Citation Status PubMed-not-MEDLINE 
520 |a This paper discusses two types of fuzzy function approximators that dynamically generate fuzzy rules with ellipsoidal regions: a function approximator based on Takagi-Sugeno type model with the center-of-gravity defuzzification and a function approximator based on a radial basis function network. Hereafter the former is called FACG and the latter is called FALC. In FACG, for each training datum the number of the training data that are within the specified distance is calculated and the training datum which has the maximum number of the training data is selected as the center of a fuzzy rule and the covariance matrix is calculated using the training data around the center. Then the parameters of the linear equation that defines the output value of the fuzzy rule are determined by the least-squares method using the training data around the center. In FALC, the training datum with the maximum approximation error is selected as the center of a fuzzy rule. Then using the training data around the center, the covariance matrix is calculated, and the parameters of a linear equation that determines the output value are calculated by the least-squares method. Performance of FACG and FALC is compared with that of multilayered neural networks and other fuzzy function approximators for the data generated by the Mackey-Glass differential equation and the data from a water purification plant 
650 4 |a Journal Article 
773 0 8 |i Enthalten in  |t IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society  |d 1996  |g 29(1999), 5 vom: 01., Seite 654-61  |w (DE-627)NLM098252887  |x 1941-0492  |7 nnns 
773 1 8 |g volume:29  |g year:1999  |g number:5  |g day:01  |g pages:654-61 
856 4 0 |u http://dx.doi.org/10.1109/3477.790450  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d 29  |j 1999  |e 5  |b 01  |h 654-61