DocumentCode
571567
Title
Improved Wavelet Networks Algorithm Research and its Application
Author
Jin-tian, Yin ; Jie, Tang ; Li, Liu
Author_Institution
Dept. of Electr. Eng., Hunan Univ. of Shaoyang, Shaoyang, China
Volume
1
fYear
2012
fDate
26-27 Aug. 2012
Firstpage
17
Lastpage
20
Abstract
The conventional learning algorithm based on BP method may converge to a local minimum, slowly converging speed and is shock before and after the Convergence point. A algorithm based on BP and PID techniques for wavelet network learning was proposed. And PIDBP algorithm can significantly reduce the probability of the emergence of local minimum after adding momentum term, At the same time introduction of the inertia term, Can be in the larger learning parameters to speed up the convergence and divergence and to reduce the possibility of oscillation, And avoid conventional BP algorithm in the convergence region Insensitivity to accelerate the convergence.
Keywords
backpropagation; convergence; learning (artificial intelligence); wavelet transforms; BP method; PID techniques; convergence region insensitivity; divergence; improved wavelet networks algorithm research; inertia term; learning algorithm; local minimum; momentum term; Convergence; Equations; Heuristic algorithms; Neural networks; Signal processing algorithms; Training; Wavelet transforms; BP algorithm; Inertia term; Momentum term; PIDBP algorithm; Wavelet Networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2012 4th International Conference on
Conference_Location
Nanchang, Jiangxi
Print_ISBN
978-1-4673-1902-7
Type
conf
DOI
10.1109/IHMSC.2012.10
Filename
6305614
Link To Document