NDLI: Identification and prediction of nonlinear multi-parameter based on least squares support vector machine

Content Provider	IEEE Xplore Digital Library
Author	Yuan-bin Hou Ning Li
Copyright Year	2012
Description	Author affiliation: The School of Electric and Control Engineering Xi'an University of Science and Technology, China (Yuan-bin Hou; Ning Li)
Abstract	The circulating fluidized bed boiler is key equipment in safety coal gangue power generation, after analyzing of the typical fault and hidden fault of main components of boiler system, and the treating methods, directed towards the nonlinear characteristics of the oxygen content of flue gas, which has many factors influence, a method based on least squares support vector machine (LS-SVM) used in flue gas oxygen content model recognition is proposed. the measured crucial parameter of influence the stable operation of the boiler are used to identification and prediction, including the oxygen content of the flue gas, coal gangue flow and material return pressure imitation, as the results show that this method has higher precision (the error is less than 7‰); Compared with general SVM and improved BP, it has higher precision and reduces the complexity of the calculation.
Starting Page	224
Ending Page	228
File Size	257455
Page Count	5
File Format	PDF
ISBN	9781467313971
e-ISBN	9781467313988
DOI	10.1109/WCICA.2012.6357872
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	identification and prediction The circulating fluidized bed boiler coal gangue power generation least squares support vector machine
Content Type	Text
Resource Type	Article

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