Submit Manuscript

Journal of Software Engineering

Year: 2015 | Volume: 9 | Issue: 3 | Page No.: 666-672
Research Article

Arithmetic Based on Wavelet Transform and Process SVM for Automatically Identifying Log-Curve Formation

Yong Yang
Northeast Petroleum University, Daqing, 163318, China
Mingtao Wu
Institute of Petroleum Economics and Management, Northeast Petroleum University, Daqing, 163318, China
Jianjun Xu
Northeast Petroleum University, Daqing, 163318, China
Arithmetic based on wavelet transform and process Support Vector Machine (SVM) for automatically identifying log-curve formation is proposed. Wavelet transform can transform any log-curve to space vector which the experiment system requires, then use the theory of process SVM automatically to identify log-curve. The results of experiment indicate this arithmetic has good identification ability and strong generalization ability on occasion that the number of training swatch is limited.
PDF Fulltext XML References Citation

Leave a Comment

Your email address will not be published. Required fields are marked *

Article Trend

Total views 1615

References

  1. Yan, L., Y. Zhu, J. Xu, W. Ren, Q. Wang and Z. Sun, 2014. Transmission lines modeling method based on fractional order calculus theory. Trans. China Electrotech. Soc., 29: 260-268.
    Direct Link
  2. Xu, J. and S. Hu, 2010. Nonlinear process monitoring and fault diagnosis based on KPCA and MKL-SVM. Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence, Volume 1, October 23-24, 2010, Sanya, China, pp: 233-237.
    CrossRef
  3. Wang, Y., Y. Zhang, J. Wang and Y. Chen, 2012. Research of flotation dosing control method based on SVM and image processing technology. Proceedings of the 31st Chinese Control Conference, July 25-27, 2012, Hefei, China, pp: 7067-7070.
  4. Gao, X. and P. Wang, 2010. Comparison studies of LS-SVM and SVM on modeling for fermentation process. Beijing Gongye Daxue Xuebao, 1: 7-12.
  5. Grbic, R., D. Sliskovic and E.K. Nyarko, 2010. Application of PLS and LS-SVM in difficult-to-measure process variable estimation. Proceedings of the 8th International Symposium on Intelligent Systems and Informatics, September 10-11, 2010, Subotica, Serbia, pp: 313-318.
    CrossRef
  6. Vapnik, V., 1995. The Nature of Statistical Learning Theory. 2nd Edn., Springer, New York, USA., ISBN-13: 9780387945590, Pages: 188.
  7. Cui, J., Y.Q. Huang, Y.B. Xie, J.J. Xu, L.M. Yan and Y.S. Zhu, 2014. Power system state estimation of quadrature Kalman filter based on PMU/SCADA measurements. Electr. Mach. Control, 18: 79-84.
    Direct Link
  8. Wang, A., M. Sha, L. Liu and F. Zhao, 2010. Fault diagnosis of TE process based on ensemble improved binary-tree SVM. Proceedings of the IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, September 23-26, 2010, Changsha, China, pp: 908-912.
    CrossRef
  9. Peng, Y., X. Zhang, Z. Han and J. Jiao, 2013. Dynamic fault diagnosis in chemical process based on SVM-HMM. Proceedings of the IEEE International Conference on Mechatronics and Automation, August 4-7, 2013, Takamatsu, Japan, pp: 1687-1691.
    CrossRef
  10. Xu, J.J., L.N. Sha, Y. Zhang, D.F. Zhang, G.C. Liu, A.H. Xu and H.Y. Li, 2011. A new algorithm for minimum spanning tree. Power Syst. Prot. Control, 39: 107-112.
    Direct Link
  11. Shang, F.H., X.J. Miao, Z.Y. Wang and S.W. Xin, 2006. Automatic identifying algorithm of water-flooded zone based on B-SVM. Proceedings of the International Conference on Machine Learning and Cybernetics, August 13-16, 2006, Dalian, China, pp: 4035-4039.
    CrossRef
  12. Pontil, M. and A. Verri, 1997. Properties of support vector machines. CBCL Paper No. 152/AI Memo No. 1612, Massachusetts Institute of Technology, Cambridge, MA., USA., August 1997.
  13. Cristianini, N., J. Kandola, A. Elisseeff and J. Shawe-Taylor, 2002. On Kernel-Target Alignment. In: Advances in Neural Information Processing Systems 14, Dietterich, T.G., S. Becker and Z. Ghahramani (Eds.). Vol. 1, MIT Press, Cambridge, MA., USA., ISBN-13: 9780262042062, pp: 367-374.
  14. Shang, F.H., T.J. Zhao and S. Li, 2003. A normalized fuzzy neural network and its application. Proceedings of the International Conference on Machine Learning and Cybernetics, Volume 2, November 2-5, 2003, Xi'an, China, pp: 1088-1091.
    CrossRef
  15. Yan, L., Y. Xie, J. Xu, C. Xue, H. Zhao and L. Bai, 2013. Improved forward and backward substitution in calculation of power distribution network with distributed generation. J. Xi'an Jiaotong Univ., 47: 117-123.
    Direct Link

Keywords