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近几年来,模糊神经网络(FNN)的研究引起了广泛的注意。本文对FNN上的反向传播学习方法加以讨论。使用输入均值和输出权重参量来进行模糊化和反模糊化处理,学习的目的是调整这两个参量到合适的值。
In recent years, the research of fuzzy neural network (FNN) has attracted a lot of attention. This article discusses the methods of back propagation learning on FNN. The input and output weights are used for fuzzification and anti-obfuscation. The purpose of learning is to adjust these two parameters to the appropriate values.