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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Representations by Back-propagating Errors. Nature 323, 533–536 (1986)
Hagan, M.T., Demuth, H.B.: Neural Network Design. China Machine Press, Beijing (2002)
Specht, D.F.: A General Regression Neural Network. IEEE Transactions on Neural Networks 2, 568–576 (1991)
Cigizoglu, H.K., Alp, M.: Generalized Regression Neural Network in Modelling River Sediment Yield. Advances in Engineering Software 37, 63–68 (2006)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, Cf., Zhang, Jb., Wang, St. (2006). Comparative Study on Input-Expansion-Based Improved General Regression Neural Network and Levenberg-Marquardt BP Network. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_9
Download citation
DOI: https://doi.org/10.1007/11816157_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
Online ISBN: 978-3-540-37273-8
eBook Packages: Computer ScienceComputer Science (R0)