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Sine neural network (SNN) with double-stage weights and structure determination (DS-WASD).
- Source :
- Soft Computing - A Fusion of Foundations, Methodologies & Applications; Jan2016, Vol. 20 Issue 1, p211-221, 11p
- Publication Year :
- 2016
-
Abstract
- To solve complex problems such as multi-input function approximation by using neural networks and to overcome the inherent defects of traditional back-propagation neural networks, a single hidden-layer feed-forward sine-activated neural network, sine neural network (SNN), is proposed and investigated in this paper. Then, a double-stage weights and structure determination (DS-WASD) method, which is based on the weights direct determination method and the approximation theory of using linearly independent functions, is developed to train the proposed SNN. Such a DS-WASD method can efficiently and automatically obtain the relatively optimal SNN structure. Numerical results illustrate the validity and efficacy of the SNN model and the DS-WASD method. That is, the proposed SNN model equipped with the DS-WASD method has great performance of approximation on multi-input function data. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14327643
- Volume :
- 20
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Soft Computing - A Fusion of Foundations, Methodologies & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 112064401
- Full Text :
- https://doi.org/10.1007/s00500-014-1491-6