Title :
Input selection for ANFIS learning
Author :
Jang, JyhShing Roger
Author_Institution :
Dept. of Comput. Sci., Nat. Tsing Hua Univ., Hsinchu, Taiwan
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;
Conference_Titel :
Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on
Conference_Location :
New Orleans, LA
Print_ISBN :
0-7803-3645-3
DOI :
10.1109/FUZZY.1996.552396
