DocumentCode
3214578
Title
Input selection for ANFIS learning
Author
Jang, JyhShing Roger
Author_Institution
Dept. of Comput. Sci., Nat. Tsing Hua Univ., Hsinchu, Taiwan
Volume
2
fYear
1996
fDate
8-11 Sep 1996
Firstpage
1493
Abstract
We present a quick and straightfoward way of input selection for neuro-fuzzy modeling using adaptive neuro-fuzzy inference systems (ANFIS). The method is tested on two real-world problems: the nonlinear regression problem of automobile MPG (miles per gallon) prediction, and the nonlinear system identification using the Box and Jenkins gas furnace data
Keywords
identification; Box and Jenkins gas furnace data; adaptive neuro-fuzzy inference systems; automobile MPG prediction; input selection; neuro-fuzzy modeling; nonlinear regression problem; nonlinear system identification; Automobiles; Buildings; Computer science; Furnaces; Fuzzy sets; Fuzzy systems; Linear regression; Nonlinear systems; Polynomials; System testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on
Conference_Location
New Orleans, LA
Print_ISBN
0-7803-3645-3
Type
conf
DOI
10.1109/FUZZY.1996.552396
Filename
552396
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