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Soft Computing

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21.10.2014 | Methodologies and Application

Sine neural network (SNN) with double-stage weights and structure determination (DS-WASD)

verfasst von: Yunong Zhang, Lu Qu, Jinrong Liu, Dongsheng Guo, Mingming Li

Erschienen in: Soft Computing | Ausgabe 1/2016

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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.

Vorheriger Artikel On interval-valued hesitant fuzzy rough approximation operators

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Titel: Sine neural network (SNN) with double-stage weights and structure determination (DS-WASD)
verfasst von: Yunong Zhang
Lu Qu
Jinrong Liu
Dongsheng Guo
Mingming Li
Publikationsdatum: 21.10.2014
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 1/2016
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-014-1491-6

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