提出了在模糊神经网络中使用Rough集理论进行网络结构设计的方法。由于Rough集理论有强大的数值分析能力，而模糊神经网络具有准确的逼近收敛能力和较高的精度，所以通过两者的结合，可以得到一种可理解性好、计算简单、收敛速度快的神经网络模型。这种网络构造方法的主要过程为：首先，利用Rough集理论对给定数据集进行规则获取；然后，根据这些规则构造模糊神经网络各层的神经元个数及相关参数初始值；最后，用BP算法迭代求出网络的各种参数，完成网络的设计。给出了一个二维非线性函数拟合的实例，进一步验证了方法的正确性。

A new method of constructing fuzzy neural network is presented and Rough set theory is applied to this method. Since Rough set theory has strong numeric analyzing ability and fuzzy neural network has exact function approaching ability, their combination can produce a neural network model with good intelligibility and fast convergence. First, some rules are acquired from given data set by rough set theory. Then, these rules are applied to constructing neural cell numbers and relative parameters in fuzzy neural network. Finally the initial network is trained by BP arithmetic and the whole network design is finished. Also in this paper, an example of nonlinear function approaching is discussed and the feasibility of this method is proved.