Computer Science ›› 2011, Vol. 38 ›› Issue (12): 236-238.

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Construction and Learning Method of Rough Logic Neural Network

  

  • Online:2018-12-01 Published:2018-12-01

Abstract: The traditional rough logic neural network can do research in information systems and decision-making and reveal the substance of rough set theory, but cannot get good results when dealing with the problem of non-singlcvalue input The rough neuron with upper and lower boundary can deal with the above problem, and with the development of rough set, the concept of upper and lower boundaries has been widely used. Comprehening the above advantages, this paper propounded the construction and studying of a kind of rough logic neural network. It is made up of rough logic neural network and rough neurons ( each variable in this pattern has both upper and lower bounds),which is called boundary rough logic neural network. First the paper gave the basic knowledge about rough neuron, rough logic and decision-making, and then propounded the structure of boundary rough logic neural network and learning methods, then gave the two models about it and compared the advantages and disadvantages between them. It indicated that this type of neural network, compared with traditional rough logic neural network, can be more efficiency when dealing with the problem non-single-valued and continuous approximation function. At last it proposed the optimized direction.

Key words: Rough neuron, Rough set, Neural network, Rough logic neural network

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