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)


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.


Input Vector Neural Network Model General Regression Neural Network Road City Remote Sensing Image 
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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

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