DocumentCode :
3452967
Title :
Nonlinear system diagnosis using neural networks and fuzzy logic
Author :
Choi, Jin Joo ; O´Keefe, Kenneth H. ; Baruah, Pranab K.
Author_Institution :
Boeing Computer Services, Seattle, WA, USA
fYear :
1992
fDate :
8-12 Mar 1992
Firstpage :
813
Lastpage :
820
Abstract :
The authors propose a real-time diagnostic system using a combination of neural networks and fuzzy logic. This neuro-fuzzy hybrid system utilizes real-time processing, prediction, and data fusion. A layer of n trained neural networks processes n independent time series (channels) which can be contaminated with environmental noise. Each network is trained to predict the future behavior of one time series. The prediction error and its rate of change from each channel are computed and sent to a fuzzy logic decision output stage, which contains n+1 modules. The (n+1)th final-output module performs data fusion by combining n individual fuzzy decisions that are tuned to match the domain expert´s need
Keywords :
diagnostic expert systems; fuzzy logic; inference mechanisms; neural nets; nonlinear systems; real-time systems; sensor fusion; data fusion; diagnostic expert system; domain expert´s need; future behavior; fuzzy decisions; neuro-fuzzy hybrid system; nonlinear system diagnosis; prediction error; real-time diagnostic system; trained neural networks; Computer networks; Fuzzy logic; Fuzzy systems; Mathematical model; Neural networks; Nonlinear systems; Predictive models; Real time systems; Robustness; Working environment noise;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems, 1992., IEEE International Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-0236-2
Type :
conf
DOI :
10.1109/FUZZY.1992.258764
Filename :
258764
Link To Document :
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