DocumentCode
3087134
Title
Research and application on improved BP neural network algorithm
Author
Xie, Rong ; Wang, Xinmin ; Li, Yan ; Zhao, Kairui
Author_Institution
Sch. of Autom., Northwestern Polytech. Univ., Xi´´an, China
fYear
2010
fDate
15-17 June 2010
Firstpage
1462
Lastpage
1466
Abstract
As the iterations are much, and the adjustment speed is slow, the improvements are made to the standard BP neural network algorithm. The momentum term of the weight adjustment rule is improved, make the weight adjustment speed more quicker and the weight adjustment process more smoother. The simulation of a concrete example shows that the iterations of the improved BP neural network algorithm can be calculated and compared. Finally, choosing a certain type of airplane as the controlled object, the improved BP neural network algorithm is used to design the control law for control command tracking, the simulation results show that the improved BP neural network algorithm can realize quicker convergence rate and better tracking accuracy.
Keywords
backpropagation; convergence; iterative methods; neural nets; airplane; control command tracking; control law; improved BP neural network algorithm; iterations; Artificial neural networks; Automation; Biological neural networks; Convergence; Feedforward neural networks; Feedforward systems; Flowcharts; Neural networks; Neurons; Standards development; convergence rate; improved BP neural networ; learning rate; momentum term; weight adjustment;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Electronics and Applications (ICIEA), 2010 the 5th IEEE Conference on
Conference_Location
Taichung
Print_ISBN
978-1-4244-5045-9
Electronic_ISBN
978-1-4244-5046-6
Type
conf
DOI
10.1109/ICIEA.2010.5514820
Filename
5514820
Link To Document