DocumentCode :
2459916
Title :
Improved ANN Algorithm Based on the Change of Search Direction
Author :
Ming, Zhao ; Zhibin, Liu ; Baosheng, Ren ; Haohan, Liu
Author_Institution :
Dagang Oilfield of PetroChina, Tianjin, China
fYear :
2010
fDate :
17-19 Dec. 2010
Firstpage :
529
Lastpage :
532
Abstract :
The essence of traditional ANN algorithm is to transfer the input-output problem of a group sample into a nonlinear programming problem. And it is a learning method to use iteration to work out weight problem along the negative gradient direction, but its convergence rate is slow and it is easy to fall into local minimum. Previously, there are many improved methods to solve the above-mentioned drawbacks by modifying the searching step size but there are few modified ANN algorithms by modifying the searching direction. In this paper, by studying the characteristics of the search direction of ANN Algorithm, there raises a new search direction, then an improved ANN algorithm based on the change of search direction is formed. The convergence rate of this new algorithm is much faster than the traditional ANN algorithm.
Keywords :
gradient methods; neural nets; nonlinear programming; search problems; improved ANN algorithm; input-output problem; negative gradient direction; nonlinear programming problem; search direction; Artificial neural networks; Computers; Convergence; Equations; Optimization; Petroleum; Search problems; artificial neural network; convergence rate; search direction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational and Information Sciences (ICCIS), 2010 International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-8814-8
Electronic_ISBN :
978-0-7695-4270-6
Type :
conf
DOI :
10.1109/ICCIS.2010.135
Filename :
5709141
Link To Document :
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