Title :
Coactive neural fuzzy modeling
Author :
Mizutani, Eiji ; Jang, Juh-Shing Roger
Author_Institution :
Dept. of Inf. Syst., Kansai Paint Co. Inc., Osaka, Japan
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;
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
DOI :
10.1109/ICNN.1995.487513
