Journal
APPLIED SOFT COMPUTING
Volume 15, Issue -, Pages 57-66Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2013.10.013
Keywords
Neural network architecture; Decay RBF neural networks; Overfitting; Noise; Stock market prediction
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Funding
- World Class University (WCU) [R32-2013-000-20014-0]
- MEST, NRF, Korea [20100020942, 2012-002521]
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In this paper, in order to optimize neural network architecture and generalization, after analyzing the reasons of overfitting and poor generalization of the neural networks, we presented a class of constructive decay RBF neural networks to repair the singular value of a continuous function with finite number of jumping discontinuity points. We proved that a function with m jumping discontinuity points can be approximated by a simplest neural network and a decay RBF neural network in L-2(R) by each epsilon error, and a function with m jumping discontinuity point y = f(x), x is an element of E subset of R-d can be constructively approximated by a decay RBF neural network in L-2(R-d) by each epsilon > 0 error. Then the whole networks will have less hidden neurons and well generalization in the same of the first part. A real world problem about stock closing price with jumping discontinuity have been presented and verified the correctness of the theory. (C) 2013 Elsevier B.V. All rights reserved.
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