NDLI: Study and apply rolling predictive control model for surrounding rock displacement based on PSO-SVM

Content Provider	IEEE Xplore Digital Library
Author	Jiang Annan Tang Chunan
Copyright Year	2008
Description	Author affiliation: Sch. of Civil & Hydraulic Eng., Dalian Univ. of Technol., Dalian (Jiang Annan; Tang Chunan)
Abstract	The excavation and construction of underground engineering is a dynamically adjusting process of system. The paper starts with the index of surrounding rock displacement which can reflect both observability and controllability of underground engineer system. The nonlinear machine learning tool - support vector machine (SVM) which based on statistic learning theory is utilized to construct the time series model. Because penalty factor and kernel parameter of SVM affect the predicting accuracy evidently, and SVM has not provided the selection method, the parameters are optimized by global optimization arithmetic - particle swarm optimization. Based on the PSO-SVM evolutionary predictive model, appending the up-to-date monitoring information, multi-step extrapolating forecast model of surrounding rock displacement is constructed, and according to control criteria, the supporting scheme is adjusted, realizing the predictive control for underground engineer. An engineer sample is studied, the result states that the PSO-SVM model is feasible. The proposed predictive control method provides new approach for underground construction.
Starting Page	1843
Ending Page	1847
File Size	289754
Page Count	5
File Format	PDF
ISBN	9781424417339
DOI	10.1109/CCDC.2008.4597642
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-02
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Surrounding rock displacement Predictive models Underground engineer Support vector machine Finite element methods Mathematical model Particle swarm optimization Monitoring Geologic measurements Convergence Predictive control
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 Libarray 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

Support vector machine based predictive controller with swarm intelligence for PEMFC

Car Sales Volume Prediction Based on Particle Swarm Optimization Algorithm and Support Vector Regression

Analysis Model of Drilling Tool Failure Based on PSO-SVM and Its Application

Classification Based on Particle Swarm Optimization for Least Square Support Vector Machines Training

Forecasting peak acceleration of blasting vibration of rock mass based on PSO-SVM

Formation drillability prediction based on PSO-SVM

PSO Algorithm for Support Vector Machine

Temperature prediction based on PSO and SVM for Roller hearth Heat Treatment Furnace

Semisupervised PSO-SVM Regression for Biophysical Parameter Estimation

Study and apply rolling predictive control model for surrounding rock displacement based on PSO-SVM

Similar Documents

Support vector machine based predictive controller with swarm intelligence for PEMFC

Car Sales Volume Prediction Based on Particle Swarm Optimization Algorithm and Support Vector Regression

Analysis Model of Drilling Tool Failure Based on PSO-SVM and Its Application

Classification Based on Particle Swarm Optimization for Least Square Support Vector Machines Training

Forecasting peak acceleration of blasting vibration of rock mass based on PSO-SVM

Formation drillability prediction based on PSO-SVM

PSO Algorithm for Support Vector Machine

Temperature prediction based on PSO and SVM for Roller hearth Heat Treatment Furnace

Semisupervised PSO-SVM Regression for Biophysical Parameter Estimation

Study and apply rolling predictive control model for surrounding rock displacement based on PSO-SVM