Application and Comparison of BP Neural Network Algorithm in MATLAB

Author

Zhao, Zhizhong ; Xin, Haiping ; Ren, Yaqiong ; Guo, Xuesong

Author_Institution

Province-Minist. Joint Key Lab. of Electromagn. Field & Electr. Apparatus Reliability, Hebei Univ. of Technol., Tianjin, China

Volume

1

fYear

2010

fDate

13-14 March 2010

Firstpage

590

Lastpage

593

Abstract

BP feed-forward network is the most widely applied neural network. There are a number of algorithms currently. The respective strengths and weaknesses of 8 kinds of BP algorithm provided by the neural network toolbox in MATLAB are studied in the paper in order to choose a more appropriate and faster algorithms under different conditions. Based on this, the measurement of vacuum level with the method of magnetron-discharge is taken as an example to carry on the simulation, the convergence steps of a variety of BP algorithm are compared in different situations, the fast convergence property of trainlm is confirmed, the conclusion is obtained that BP algorithm can forecast the vacuum level.

Keywords

backpropagation; convergence of numerical methods; feedforward neural nets; mathematics computing; vacuum measurement; BP feedforward network; BP neural network algorithm; MATLAB; magnetron-discharge; neural network toolbox; trainlm fast convergence property; vacuum level measurement; Artificial neural networks; Computer errors; Convergence; Feedforward neural networks; Feedforward systems; MATLAB; Magnetic field measurement; Multi-layer neural network; Neural networks; Neurons; Back-propagation Algorithm; Method of Magnetron-discharge; Neural Network; Vacuum Level;

fLanguage

English

Publisher

ieee

Conference_Titel

Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on

Conference_Location

Changsha City

Print_ISBN

978-1-4244-5001-5

Electronic_ISBN

978-1-4244-5739-7

Type

conf

DOI

10.1109/ICMTMA.2010.492

Filename

5458945