DocumentCode
619909
Title
Varied order iterative learning law for BPNN
Author
XiaoLei Chen ; Ning Chen
Author_Institution
Nanjing Forestry Univ., Nanjing, China
fYear
2013
fDate
25-27 May 2013
Firstpage
1364
Lastpage
1369
Abstract
In this paper, we discussed respective superiority what back propagation neural network based on fractional differential and integer-order differential have, from two aspects-convergent speed and error. Then in order to get better convergent effect, the paper proposes the neural network of adaptive order. The detailed progress what is verified by MATLAB is illustrated in figures as follows.
Keywords
backpropagation; differential equations; iterative methods; neural nets; BPNN; MATLAB; adaptive order; aspects-convergent error; aspects-convergent speed; back propagation neural network; convergent effect; fractional differential; integer-order differential; varied order iterative learning law; Adaptive systems; Biological neural networks; Convergence; Fractional calculus; Neurons; Training; Back propagation neural network; Fractional differential; Integer-order differential;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location
Guiyang
Print_ISBN
978-1-4673-5533-9
Type
conf
DOI
10.1109/CCDC.2013.6561138
Filename
6561138
Link To Document