DocumentCode
3257846
Title
Coactive neural fuzzy modeling
Author
Mizutani, Eiji ; Jang, Juh-Shing Roger
Author_Institution
Dept. of Inf. Syst., Kansai Paint Co. Inc., Osaka, Japan
Volume
2
fYear
1995
fDate
Nov/Dec 1995
Firstpage
760
Abstract
We discuss the neuro-fuzzy modeling and learning mechanisms of CANFIS (coactive neuro-fuzzy inference system) wherein both neural networks and fuzzy systems play active roles together in an effort to reach a specific goal. Their mutual dependence presents unexpected learning capabilities. CANFIS has extended the basic ideas of its predecessor ANFIS (adaptive network-based fuzzy inference system): the ANFIS concept has been extended to any number of input-output pairs. In addition, CANFIS yields advantages from nonlinear fuzzy rules. In light of some model-related limitations, this paper serves to highlight both neuro-fuzzy learning capacities and practical obstacles encountered in performing neuro-fuzzy modeling
Keywords
fuzzy neural nets; inference mechanisms; knowledge based systems; learning (artificial intelligence); modelling; CANFIS; coactive neuro-fuzzy inference system; fuzzy systems; learning mechanisms; neural fuzzy modeling; neural networks; neuro-fuzzy learning; nonlinear fuzzy rules; Neural networks; Neurons;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location
Perth, WA
Print_ISBN
0-7803-2768-3
Type
conf
DOI
10.1109/ICNN.1995.487513
Filename
487513
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