NDLI: Fuzzy discrimination analysis method based on RBFNN and its application in soft measurement

Content Provider	IEEE Xplore Digital Library
Author	Gao lin Liu xi-mei Gu Xing-sheng Sui yuan-yuan Zhuang ke-yu
Copyright Year	2008
Description	Author affiliation: East China Univ. of Sci. & Technol., Shanghai (Gao lin; Liu xi-mei; Gu Xing-sheng)
Abstract	Artificial neural networks(ANN) has being used widely in information processing, intelligence control because of its abilities of self-organization, self-learning and parallel-processing. The work to use ANN theory and Fuzzy sets together to solve the practical problem is being promoted with the fuzzy sets birth and development. Based on the basic theory of RBFNN and fuzzy sets, a new fuzzy discrimination analysis method named RFD(fuzzy discrimination based on RBFNN) is proposed in this paper. It includes three steps to construct RFD, that is classification earmark, modeling by RBFNN and fuzzy reasoning. After explaining in detail the process of the above three steps to construct RFD. It is tested for discriminating some UCIs such as the IRIS data and a practical soft measurement data. The results indicate that RFD has low error- discrimination percentage, short discrimination time and satisfied practical application effect.
Starting Page	2603
Ending Page	2607
File Size	161301
Page Count	5
File Format	PDF
ISBN	9781424425020
DOI	10.1109/ICAL.2008.4636611
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Iris Analytical models Measurement uncertainty Artificial neural networks Artificial Neural Networks. Fuzzy sets. RBFNN. Discrimination analysis. Soft measurement Fuzzy reasoning Testing
Content Type	Text
Resource Type	Article

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