Abstract
The back propagation (BP) neural network algorithm is a multi-layer feedforward network trained according to error back propagation algorithm and is one of the most widely applied neural network models. BP network can be used to learn and store a great deal of mapping relations of input-output model, and no need to disclose in advance the mathematical equation that describes these mapping relations. Its learning rule is to adopt the steepest descent method in which the back propagation is used to regulate the weight value and threshold value of the network to achieve the minimum error sum of square. This paper focuses on the analysis of the characteristics and mathematical theory of BP neural network and also points out the shortcomings of BP algorithm as well as several methods for improvement.
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
Yan, P., Huang, R.: Artificial Neural Network — Model, Analysis and Application. Anhui Educational Publishing House, Hefei
Zhou, K., Kang, Y.: Neural Network Models and MATLAB Simulation Program Design. Tsinghua University Press, Beijing
Gao, J.: Artificial Neural Network Theory and Simulation Examples. China Machine Press, Beijing
Liu, J.: Intelligent Control. Electronic Industry Press, Beijing
Wang, X., Cao, L.: Genetic Algorithm – Theory, Application and Software Implementation. Xi’an Jiaotong University Press
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag GmbH Berlin Heidelberg
About this paper
Cite this paper
Li, J., Cheng, Jh., Shi, Jy., Huang, F. (2012). Brief Introduction of Back Propagation (BP) Neural Network Algorithm and Its Improvement. In: Jin, D., Lin, S. (eds) Advances in Computer Science and Information Engineering. Advances in Intelligent and Soft Computing, vol 169. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30223-7_87
Download citation
DOI: https://doi.org/10.1007/978-3-642-30223-7_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30222-0
Online ISBN: 978-3-642-30223-7
eBook Packages: EngineeringEngineering (R0)