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Proceedings Paper

A study on learning mechanism for neuron networks with weight-function
Author(s): Xi Huang; Zong-huang Weng; Wen-zao Shi; Ping Wang
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Paper Abstract

In this paper a new neural network model with weight-function is proposed. In the model, the weight is a function with adjustable parameters, and the sum of these weight functions as the neuron output. And according to BP algorithm, the learning algorithm of feed-forward neural network with weight-function neurons is studied. Simulation results show that, applying the back-propagation algorithm to the new neural network the better convergence rate can be obtained and in some applications the new neural network based on the weight-function neurons is superior to the BP network based on the MP neuron model, so that it has a significant value in further research and application.

Paper Details

Date Published: 13 March 2013
PDF: 6 pages
Proc. SPIE 8784, Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies, 87840L (13 March 2013); doi: 10.1117/12.2013811
Show Author Affiliations
Xi Huang, Fujian Normal Univ. (China)
Zong-huang Weng, Fujian Normal Univ. (China)
Wen-zao Shi, Fujian Normal Univ. (China)
Ping Wang, Fujian Normal Univ. (China)


Published in SPIE Proceedings Vol. 8784:
Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies
Yulin Wang; Liansheng Tan; Jianhong Zhou, Editor(s)

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