Advertisement

Comparative Study on Input-Expansion-Based Improved General Regression Neural Network and Levenberg-Marquardt BP Network

  • Chao-feng Li
  • Jun-ben Zhang
  • Shi-tong Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

Abstract

The paper presents an input-expansion-based improved method for general regression neural network (GRNN) and BP network. Using second-order inner product function or Chebyshev polynomial function to expand input vector of original samples, which makes input vector mapped into a higher-dimension pattern space and thus leads to the samples data more easily separable. The classification results for both Iris data and remote sensing data show that general regression neural network is superior to Levenberg-Marquardt BP network (LMBPN) and moreover input-expansion method may efficiently enhance classification accuracy for neural network models.

Keywords

Input Vector Neural Network Model General Regression Neural Network Road City Remote Sensing Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Tomand, D., Schover, A.: A Modified General Regression Neural Network (MGRNN) with New Efficient Training Algorithms as a Robust ‘Black Box’-tool for Data Analysis. Neural networks 14, 1023–1034 (2002)CrossRefGoogle Scholar
  2. 2.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Representations by Back-propagating Errors. Nature 323, 533–536 (1986)CrossRefGoogle Scholar
  3. 3.
    Hagan, M.T., Demuth, H.B.: Neural Network Design. China Machine Press, Beijing (2002)Google Scholar
  4. 4.
    Specht, D.F.: A General Regression Neural Network. IEEE Transactions on Neural Networks 2, 568–576 (1991)CrossRefGoogle Scholar
  5. 5.
    Cigizoglu, H.K., Alp, M.: Generalized Regression Neural Network in Modelling River Sediment Yield. Advances in Engineering Software 37, 63–68 (2006)CrossRefGoogle Scholar
  6. 6.
    Hyun, B.G., Nam, K.: Faults Diagnoses of Rotaing Machines by Using Neural Nets: GRNN and BPNN. In: Proceedings of the 1995 IEEE IECON 21st International Conferece on Industrial ECI, Orland (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chao-feng Li
    • 1
  • Jun-ben Zhang
    • 1
  • Shi-tong Wang
    • 1
  1. 1.School of Information TechnologySouthern Yangtze UniversityWuxiChina

Personalised recommendations