Home            Contact us            FAQs
    
      Journal Home      |      Aim & Scope     |     Author(s) Information      |      Editorial Board      |      MSP Download Statistics

     Research Journal of Applied Sciences, Engineering and Technology

    Abstract
2013(Vol.5, Issue:20)
Article Information:

Hybrid PCA/SVM Method for Recognition of Non-Stationary Time Series

Shao Qiang and Feng Chanjian
Corresponding Author:  Shao Qiang 
Submitted: September 27, 2012
Accepted: November 11, 2012
Published: May 15, 2013
Abstract:
A SVM (Support Vector Machine)-like framework provides a novel way to learn linear Principal Component Analysis (PCA), which is easy to be solved and can obtain the unique global solution. SVM is good at classification and PCA features are introduced into SVM. So, a new recognition method based on hybrid PCA and SVM is proposed and used for a series of experiments on non-stationary time series. The results of non-stationary time series recognition and prediction experiments are presented and show that the method proposed is effective.

Key words:  Chatter gestation, pattern recognition, PCA, SVM, , ,
Abstract PDF HTML
Cite this Reference:
Shao Qiang and Feng Chanjian, . Hybrid PCA/SVM Method for Recognition of Non-Stationary Time Series. Research Journal of Applied Sciences, Engineering and Technology, (20): 4857-4861.
ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
Submit Manuscript
   Information
   Sales & Services
Home   |  Contact us   |  About us   |  Privacy Policy
Copyright © 2024. MAXWELL Scientific Publication Corp., All rights reserved