A Robust Back Propagation Algorithm for Function Approximation


The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 4, No. 3, pp. 747-753, Mar. 1997
10.3745/KIPSTE.1997.4.3.747,   PDF Download:

Abstract

Function approximation from a set of input-output pairs has numerous applications in scientific and engineering areas. Multilayer feedforward neural networks have been proposed as a good approximator of nonlinear function. The back propagation (BP) algorithm allows multilayer feedforward neural networks to learn input-output mappings from training samples. However, the mapping acquired through the BP algorithm may be corrupt when errorneous training data are employed. In this paper we propse a robust BP learning algorithm that is resistant to the errorneous data and is capable of rejecting gross errors during the approximation process.


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Cite this article
[IEEE Style]
K. S. Min, H. C. Ha, O. K. Sik, "A Robust Back Propagation Algorithm for Function Approximation," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 4, no. 3, pp. 747-753, 1997. DOI: 10.3745/KIPSTE.1997.4.3.747.

[ACM Style]
Kim Sang Min, Hwang Chang Ha, and Oh Kwang Sik. 1997. A Robust Back Propagation Algorithm for Function Approximation. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 4, 3, (1997), 747-753. DOI: 10.3745/KIPSTE.1997.4.3.747.