DocumentCode :
2592423
Title :
Input Values Function for Improving Generalization Capability of BP Neural Network
Author :
He, Tongzhi ; Zheng, Shijue ; Zhang, Ping ; Zou, Ming
Author_Institution :
Dept. of Comput. Sci., Hua Zhong Normal Univ., Wuhan, China
fYear :
2010
fDate :
17-18 April 2010
Firstpage :
228
Lastpage :
231
Abstract :
As is known to all, Back propagation (BP) neural network has two important advantage and disadvantage: learning speed and generalization capability (GC). In this paper, we propose a new method by adding input values function (IVF) to improve the generalization of BP neural network. The result shows: GC in a certain extent has been improved throught this method. But if we want to do much more promoting in various fields, there are still a lot of things that we must to be studied.
Keywords :
backpropagation; neural nets; BP Neural Network; GC; back propagation; generalization capability improvement; input values function; Algorithm design and analysis; Computer science; Education; Feedforward systems; Helium; Multi-layer neural network; Neural networks; Neurons; System testing; Wearable computers; BP neural network; Generalization Capability; Input Values Function;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wearable Computing Systems (APWCS), 2010 Asia-Pacific Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4244-6467-8
Electronic_ISBN :
978-1-4244-6468-5
Type :
conf
DOI :
10.1109/APWCS.2010.64
Filename :
5480505
Link To Document :
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