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中文摘要：
本文研究了基于变精度粗糙集模型下的粗集神经网络设计，对β近似约简条件进行了弱化推广，同时提出了口近似约简的选取原则。在对Brodatz纹理图像的分类实验中，比较了经典粗集神经网络RNN和变精度粗集神经网络VPRNN的性能，VPRNN不仅具有更为精简的结构和更短的训练时间，而且具有更强的近似决策和泛化能力。
英文摘要：
The design of the rough neural network based on variable precision rough set model is studied. The condition of β-approximation reduction is generalized and the criteria for selecting a β -approximation reduction are introduced. In the experiment of the Brodatz texture image classification, the performance of conventional RNN（Rough Neural Network） and VPRNN（Variable Precision Rough set Neural Network） is compared. The results indicate that VPRNN not only has more simplify structure and less training time, but also, has better approximation decision-making ability and generalization ability than RNN.
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