DocumentCode :
3002392
Title :
Fuzzy discrimination analysis method based on RBFNN and its application in soft measurement
Author :
Lin, Gao ; Xi-mei, Liu ; Xing-sheng, Gu ; Yuan-yuan, Sui ; Ke-yu, Zhuang
Author_Institution :
East China Univ. of Sci. & Technol., Shanghai
fYear :
2008
fDate :
1-3 Sept. 2008
Firstpage :
2603
Lastpage :
2607
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.
Keywords :
fuzzy reasoning; fuzzy set theory; neural nets; ANN theory; RBFNN; artificial neural networks; classification; fuzzy discrimination analysis; fuzzy reasoning; fuzzy sets; information processing; intelligence control; parallel processing; self learning; self organization; soft measurement; Artificial intelligence; Artificial neural networks; Fuzzy reasoning; Fuzzy set theory; Fuzzy sets; Information processing; Intelligent control; Intelligent networks; Process control; Testing; Artificial Neural Networks. Fuzzy sets. RBFNN.; Discrimination analysis. Soft measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Automation and Logistics, 2008. ICAL 2008. IEEE International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-2502-0
Electronic_ISBN :
978-1-4244-2503-7
Type :
conf
DOI :
10.1109/ICAL.2008.4636611
Filename :
4636611
Link To Document :
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