NDLI: Chaotic time series forecasting based on SVM for silicon content in hot metal

Content Provider	IEEE Xplore Digital Library
Author	Wang Yikang Liu Xiangguan
Copyright Year	2014
Description	Author affiliation: Dept. of Math., Zhejiang Univ., Hangzhou, China (Liu Xiangguan) \|\| Dept. of Math., China Jiliang Univ., Hangzhou, China (Wang Yikang)
Abstract	A chaotic time series forecasting model based on support vector machine(SVM) for silicon content in hot metal is proposed which combines the support vector machine and chaotic forecasting theory. The original silicon content time series is reconstructed to a high dimension space through the skills of state space reconstruction. The training sample and testing sample are obtained based on the states in the state space, and then the support vector machine theory is used for forecasting. The simulation results show that the proposed model has better curve fitting and higher forecasting accuracy compared to that of RBF, AOLM and Volterra adaptive model. The hit rate reaches 88% in successive 100 heats in test set in the range of [Si] 0.1%. It seems promising and determinant in providing the experts with the right tools for the prediction in this difficult problem, and it can satisfy the requirements of on-line prediction of silicon content in hot metal. It develops the theory and method for silicon content forecasting in hot metal.
Starting Page	5156
Ending Page	5161
File Size	145900
Page Count	6
File Format	PDF
ISBN	9789881563873
ISSN	19341768
DOI	10.1109/ChiCC.2014.6895818
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	TCCT, CAA
Subject Keyword	Support vector machines support vector machine Time series analysis silicon content in hot metal Metals Predictive models Blast furnaces Silicon Mathematical model state space reconstruction chaotic time series
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Comparison of univariate and multivariate predicted method based on support vector regression for silicon content in hot metal

A New Model Based on GRA and LSSVM to Predict Silicon Content in Hot Metal

Modeling hot metal silicon content in blast furnace based on locally weighted SVR and mutual information

Prediction of Silicon Content in Hot Metal Based on the Combined Model of EMD and SVM

Multiscale models on time series of silicon content in blast furnace hot metal based on Hilbert-Huang transform

Prediction of silicon content in hot metal based on RS-LSSVM model

Prediction of hot metal silicon content in blast furnace based on EMD and DNN

The prediction model of silicon content in hot metal based on LS-SVR optimized by estimation distributed algorithm

Final sulfur content prediction model in hot metal desulphurization process based on IEA-SVM

Chaotic time series forecasting based on SVM for silicon content in hot metal

Similar Documents

Comparison of univariate and multivariate predicted method based on support vector regression for silicon content in hot metal

A New Model Based on GRA and LSSVM to Predict Silicon Content in Hot Metal

Modeling hot metal silicon content in blast furnace based on locally weighted SVR and mutual information

Prediction of Silicon Content in Hot Metal Based on the Combined Model of EMD and SVM

Multiscale models on time series of silicon content in blast furnace hot metal based on Hilbert-Huang transform

Prediction of silicon content in hot metal based on RS-LSSVM model

Prediction of hot metal silicon content in blast furnace based on EMD and DNN

The prediction model of silicon content in hot metal based on LS-SVR optimized by estimation distributed algorithm

Final sulfur content prediction model in hot metal desulphurization process based on IEA-SVM

Chaotic time series forecasting based on SVM for silicon content in hot metal