Rough Neural Networks: A Review
نویسندگان
چکیده
The rough neural networks (RNNs) are the neural networks based on rough set and one kind of hot research in the artificial intelligence in recent years, which synthesize the advantage of rough set to process uncertainly question: attributes reduce by none information losing then extract rule, and the neural networks have the strongly fault tolerance, self-organization, massively parallel processing and self-adapted. So that RNNs can process the massively and uncertainly information, which is widespread applied in our life. This article summarizes the recent research development of RNNs. First introduce the theory of rough set and the rough neuron; next analyze the RNNs in following four aspects: the neural network based on using rough set in preprocessing information, the neural networks base on rough logic, the neural networks based on rough neuron and the neural networks based on rough-granular; then give the flow chart of the RNNs processing question and the application of the classical neural network based on rough set; last give some advice to the development of RNNs in the future.
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