在对可加性模糊系统的最重要的组成部分标准可加性模型（SAM）进行研究的基础上,把SAM系统与神经网络相结合,并推导出适合此网络结构的简化的隶属函数参数的学习算法,从仿真结果可知该方法简单、学习次数少、逼近精度高. Based on the research on the standard additive model (SAM), which is the most important part of additive fuzzy systems, the SAM system is combined with neural network and the simplified membership function parameters suitable for this network structure are derived Learning algorithm, the simulation results show that the method is simple, less learning, approximation accuracy.