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An overview on rough neural networks

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Abstract

This paper is based on rough set theory and neural networks, and mainly introduces the previous researchers how to use rough set theory, which has the superior ability to rule out redundant, and neural networks, which has the self-organizing and self-learning ability to complement each other’s advantages, in order to obtain rough neural networks with better performance. This paper also details the possibility of the integration of these two theories and the current mainstream fusion method and then takes two more mainstream previous neural networks, back-propagation neural networks and radial basis function neural networks, as an example to integrate with rough set theory. This example describes the fusion method, fusion performance, and its corresponding learning algorithm after fusion in detail.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61379101).

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China

    Hongmei Liao, Shifei Ding, Miaomiao Wang & Gang Ma

  2. Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, China University of Mining and Technology, 221116, Xuzhou, China

    Shifei Ding

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  1. Hongmei Liao
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  2. Shifei Ding
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  3. Miaomiao Wang
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  4. Gang Ma
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Correspondence to Shifei Ding.

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Liao, H., Ding, S., Wang, M. et al. An overview on rough neural networks. Neural Comput & Applic 27, 1805–1816 (2016). https://doi.org/10.1007/s00521-015-2009-6

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  • DOI: https://doi.org/10.1007/s00521-015-2009-6

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