基于BP算法的模糊神经网络研究

FUZZY NEURAL NETWORK BASED ON BP AIGORITHM

提出了一种基于模糊集合与前馈神经网络结合的神经网络。这种模糊神经网络（ＦＮＮ）在处理模糊特征时，能较好地反映出输入值和输出值的隶属度关系。应用经典的ＢＰ算法对网络进行训练。为了加快网络的学习过程，我们介绍了一种调整权值和阈值的学习方法，并且采用了Ｃ语言编制软件。实验结果表明，ＦＮＮ技术是一种新颖、有效的方法，能促进智能神经网络的发展。

model of fuzzy neural network(FNN)based on fuzzy sets and feed forward neUral network is presented.The FNN can describe a relationship of value of the membership function of input and output data. A cias-'sic BP algorithm is used. In order to accelerate learning process,a method is introduced to adjust weights and thresholds,also a software is programmed in C language. Experiments and results demonstrate that FNN technique is a novel and potetially powerful approach toward intelligent neural network.