NDLI: Prediction System of Burning Through Point (BTP) Based on Adaptive Pattern Clustering and Feature Map

Content Provider	IEEE Xplore Digital Library
Author	Wu-Shan Cheng
Copyright Year	2006
Description	Author affiliation: Dept. of Intelligent Robotics, Shanghai Univ. of Eng. Sci. (Wu-Shan Cheng)
Abstract	In this paper, due to the property of the long time delay, time varying and multimode of sintering process, an adaptive pattern clustering and feature map (APCFM) is proposed to solve the challenging problem for building a predictive system of burning through point. By using the density clustering and learning vector quantization (LVQ), the whole vector is divided automatically into subclasses which have similar clustering center and labeled fitting number, then these labeled subclasses samples are token into genetic neural network (GNN) to train. The training set consists 707 groups of actual process data and GNN are trained with APCFM algorithm, these experiments proved that this system is stronger robust and generality in clustering analysis and feature extraction
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	3089
Ending Page	3094
File Size	845539
Page Count	6
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.258372
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pattern clustering Delay effects Time varying systems Vector quantization Genetics Neural networks Clustering algorithms Robustness Algorithm design and analysis Feature extraction GNN Burning through point (BTP) pattern clustering feature map APCFM
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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