一种基于Rough Sets和模糊神经网络的规则获取的方法

该文提出了一种基于RoughSets思想获取初始规则，并通过模糊神经网络优化，最后再进行简化获取模糊规则，及模糊系统参数学习的方法。并通过实例进行了自动列车运行系统仿真。文中还基于上述实例，将这种基于模糊神经网络的学习与控制方法与标准的BP网络和基本的模糊系统方法进行了比较，并总结了这种方法的特点。结论表明，该文所提出的模糊规则生成和模糊系统学习方法是行之有效的。

A Method of Fuzzy Rules Acquisition Based on Rough Sets and a Fuzzy Neural Network

This paper proposes a method of fuzzy rules determination by acquiring original fuzzy rules based on rough sets, optimizing the fuzzy rules based on a fuzzy neural network, and reducing the fuzzy rules. A method of fuzzy system parameters tuning based on the fuzzy neural network is also proposed in the paper. An example is used to perform automatic train operation simulation. The example is also applied to compare the fuzzy neural network learning and control with standard BP network and basic fuzzy system learning and control methods, and the characteristics of the method is summed up. Finally, Conclusion indicates that methods of fuzzy rules generation and fuzzy system tuning is very effective.