Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


Thesis Xiaohui Song.pdf (1.17 MB)

ETD Abstract Container

Abstract Header

FPGA Implementation of a Support Vector Machine based Classification System and its Potential Application in Smart Grid

Song, Xiaohui
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1376579033

Abstract Details

2013, Master of Science in Electrical Engineering, University of Toledo, College of Engineering.
Support Vector Machines (SVMs) is a popular classification and regression prediction tool that uses supervised machine learning theory to maximize the predictive accuracy. This paper focuses on the field programmable gate array (FPGA) implementation of a Support Vector Machine classification system. Owing to the advanced parallel calculation feature provided by FPGA, a fast data classification can be achieved by the FPGA-based two-class SVM classifier. The classification system works both in linear mode or non-linear mode, depending on the dimensions of the classification. Simulated results demonstrate that the classification system is effective in fast data classification, as well as a promising technique used in Smart Grid to strengthen the communication security.
Hong Wang, Dr. (Committee Chair)
Lingfeng Wang, Dr. (Committee Co-Chair)
Weiqing Sun, Dr. (Committee Member)
64 p.
Electrical Engineering
Data classification; field programmable gate array; parallel processing; support vector machines; smart grid

Recommended Citations

Citations

  • Song, X. (2013). FPGA Implementation of a Support Vector Machine based Classification System and its Potential Application in Smart Grid [Master's thesis, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1376579033

    APA Style (7th edition)

  • Song, Xiaohui. FPGA Implementation of a Support Vector Machine based Classification System and its Potential Application in Smart Grid. 2013. University of Toledo, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1376579033.

    MLA Style (8th edition)

  • Song, Xiaohui. "FPGA Implementation of a Support Vector Machine based Classification System and its Potential Application in Smart Grid." Master's thesis, University of Toledo, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1376579033

    Chicago Manual of Style (17th edition)

toledo1376579033
8,089
© 2013, some rights reserved.Creative Commons License FPGA Implementation of a Support Vector Machine based Classification System and its Potential Application in Smart Grid by Xiaohui Song is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Toledo and OhioLINK.