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探讨了将模糊规则的提取和推理转化为人工神经网络参数的确定及神经计算，提出一种具有自学习功能的模糊神经网络（ＦＮＮ），并用因子动态调整逼近法，解决了系统中静态误差和积分饱和等问题．
This paper discusses how to convert the extraction and reasoning of fuzzy rules into parameters of artificial neural network and the neural computation. A fuzzy neural network (FNN) with self-learning function is proposed. By using the factor dynamic adjustment approximation method, the static error and Integral saturation and other issues.