Abstract
In this paper, the RBF neural network case teaching has been studied. In the actual teaching process, we find it more difficult for student to learn the course, duing to the RBF neural network curriculum theory is more stronger. Many students do not know how to use the theory to solve practical problems.Therefore, we equip students with basic knowledge of RBF neural network through case teaching. In this paper , a RBF neural network example has been analysised and applied to enable the students to learn RBF neural network. Teaching practice shows that case teaching can achieve better teaching effectiveness.
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
Wei, H.-k.: Neural network structure design theory and method. National Defence Industry Press, Beijing (2005)
Sun, Y.-p., Zhang, L., Lv, R.-y.: Tourist quantity forecast by using neural network. Human Geography 17(6), 50–52 (2002)
Li, J.-b., Zhang, H.-q.: Application of BP neural network model on forecasting the number of tourists in Hainan province. In: 2010 International Conference on Computer and Communication Technologies in Agriculture Engineering (CCTAE) (2010)
Zhang, D.-f.: MATLAB Neural Network Application Design. Mechanical Industry Press, Beijing (January 2009)
Zhang, D.-f.: MATLAB Neural Network Simulation and Application. Electronic Industry Press, Beijing (June 2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, J., Zhang, H., Zhou, Y., Bai, Y. (2011). RBF Neural Network Case Teaching Research. In: Tan, H., Zhou, M. (eds) Advances in Information Technology and Education. Communications in Computer and Information Science, vol 201. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22418-8_49
Download citation
DOI: https://doi.org/10.1007/978-3-642-22418-8_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22417-1
Online ISBN: 978-3-642-22418-8
eBook Packages: Computer ScienceComputer Science (R0)