Title :
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
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;
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
DOI :
10.1109/ICMTMA.2010.492
