Please wait, while we are loading the content...
Please wait, while we are loading the content...
Content Provider | IEEE Xplore Digital Library |
---|---|
Author | Yujun Li Xiaojun Tang Junhua Liu |
Copyright Year | 2009 |
Description | Author affiliation: School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, 710049, China (Yujun Li; Xiaojun Tang; Junhua Liu) |
Abstract | In order to improve the pressure sensor's current stability and temperature drift performance, a soft sensor regression model was modeled based on least square support vector machine (LS-SVM). According to the difficulty in selecting penalty factor and kernel parameter which are called hyper-parameters in LS-SVM when modeling, particle swarm optimization (PSO) algorithm and ergodicity search algorithm (ESA) are proposed to optimize it. Then a soft sensor model would be reconstructed according to the optimal hyper-parameters. The experiment results show that the time of optimizing the hyper-parameters by PSO (about 1676 second) is decreased to that of ESA (about 211884 second), and mean squared error (MSE) of the prediction model with optimal parameters got by PSO (about $1.25×10^{-6})$ is reduced to one sixth of that by ESA (about $8.35×10^{-6}).$ So PSO algorithm has more superior performance on global optimization and convergence speed. The optimal soft pressure sensor model got by PSO has more superior current stability and temperature drift performance, and the disadvantage influence of the change of working current and circumstance temperature on the sensor is decreased greatly. This method is feasible that combining PSO algorithm with LS-SVM in soft sensor modeling. It has definite development space and practical application value. |
File Size | 130649 |
File Format | |
ISBN | 9781424438631 |
DOI | 10.1109/ICEMI.2009.5274419 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2009-08-16 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Stability Predictive models particle swarm optimization Particle swarm optimization Least squares methods Convergence Temperature sensors Support vector machines Neural networks Support vector machine classification sensor least square support vector machine hyper-parameters Kernel |
Content Type | Text |
Resource Type | Article |
National Digital Library of India (NDLI) is a virtual repository of learning resources which is not just a repository with search/browse facilities but provides a host of services for the learner community. It is sponsored and mentored by Ministry of Education, Government of India, through its National Mission on Education through Information and Communication Technology (NMEICT). Filtered and federated searching is employed to facilitate focused searching so that learners can find the right resource with least effort and in minimum time. NDLI provides user group-specific services such as Examination Preparatory for School and College students and job aspirants. Services for Researchers and general learners are also provided. NDLI is designed to hold content of any language and provides interface support for 10 most widely used Indian languages. It is built to provide support for all academic levels including researchers and life-long learners, all disciplines, all popular forms of access devices and differently-abled learners. It is designed to enable people to learn and prepare from best practices from all over the world and to facilitate researchers to perform inter-linked exploration from multiple sources. It is developed, operated and maintained from Indian Institute of Technology Kharagpur.
Learn more about this project from here.
NDLI is a conglomeration of freely available or institutionally contributed or donated or publisher managed contents. Almost all these contents are hosted and accessed from respective sources. The responsibility for authenticity, relevance, completeness, accuracy, reliability and suitability of these contents rests with the respective organization and NDLI has no responsibility or liability for these. Every effort is made to keep the NDLI portal up and running smoothly unless there are some unavoidable technical issues.
Ministry of Education, through its National Mission on Education through Information and Communication Technology (NMEICT), has sponsored and funded the National Digital Library of India (NDLI) project.
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 |
Loading...
|