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Granular neural networks

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Abstract

Fuzzy neural networks (FNNs) and rough neural networks (RNNs) both have been hot research topics in the artificial intelligence in recent years. The former imitates the human brain in dealing with problems, the other takes advantage of rough set theory to process questions uncertainly. The aim of FNNs and RNNs is to process the massive volume of uncertain information, which is widespread applied in our life. This article summarizes the recent research development of FNNs and RNNs (together called granular neural networks). First the fuzzy neuron and rough neuron is introduced; next FNNs are analysed in two categories: normal FNNs and fuzzy logic neural networks; then the RNNs are analysed in the following four aspects: neural networks based on using rough sets in preprocessing information, neural networks based on rough logic, neural networks based on rough neuron and neural networks based on rough-granular; then we give a flow chart of the RNNs processing questions and an application of classical neural networks based on rough sets; next this is compared with FNNs and RNNs and the way to integrate is described; finally some advice is given on development of FNNs and RNNs in future.

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Shifei Ding, Hongjie Jia & Jinrong Chen

  2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Shifei Ding

  3. Geomatics College, Shandong University of Science and Technology, Qingdao, 266510, China

    Fengxiang Jin

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  1. Shifei Ding
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  2. Hongjie Jia
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  3. Jinrong Chen
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  4. Fengxiang Jin
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Correspondence to Shifei Ding.

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Ding, S., Jia, H., Chen, J. et al. Granular neural networks. Artif Intell Rev 41, 373–384 (2014). https://doi.org/10.1007/s10462-012-9313-7

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