目前，模糊神经网络控制在控制领域已成为一个研究热点。把神经网络应用于模糊系统，可以解决模糊系统中的知识抽取问题；把模糊系统应用于神经网络，神经网络就不再是黑箱了，人类的知识就很容易融合到神经网络中。本文提出了一种新型的动态模糊神经网络的结构及其学习算法，该动态模糊神经网络的结构基于扩展的径向基网络。其学习算法的最大特点是参数的调整和结构的辨识同时进行，且学习速度快，可用于实时建模与控制。开发了相关的算法程序，最后针对实际案例进行了仿真分析。仿真结果表明，动态模糊神经网络具有学习速度快、系统结构紧凑、泛化能力强等优点。

The fuzzy neural network control in the control domain has already become a hot topic at present. Applying the neural network in fuzzy systems may solve the fuzzy systems' knowledge extraction problem. Applying the fuzzy system in neural networks, the neural network is no longer a black box, and humanity's knowledge is very easy to fuse in the neural network. We propose a new fuzzy neural network structure and its learning algorithm based on the expansion radial direction base network. Its learning algorithm's most major characteristics are the parameter adjustment and the structure identification perform concurrently, and the study speed is high, which may be used in realtime modelling and controling. We develop the related algorithm routines, and finally carry out a simulation analysis as for real cases. The simulation result indicates that the dynamic fuzzy neural network is fast, compact,capable in generalization.