一种新型模糊神经网络结构确定的研究

Study on Determination of the Structure of an Improved Fuzzy-Neural Network

本文给出了一种改进的模糊神经网络模型。这种模糊神经网络结构的建立只需要根据系统的输入输出数据来得到，且含义非常明晰。通常网络神经元的个数根据经验来推测，而我们通过模糊数据曲线来确定网络模型，具有更高的准确度和优化性。模型的模糊隶属度函数和网络仅值可通过高速BP神经网络学习算法来得到；结果表明该系统模型的学习和预测效果非常好。

This paper presents and improved fuzzy-neural network(FNN) model, which is a simple but effective fuzzy-rule based model.Its architecture could be determined by only using the input-output data of the system .In the FNN model, the membership functions and weights would be adjusted optimally through high speed BP learning algorithm, and the number of neurons in the inference layer would be confirmed through fuzzy data curves .The neural network can be considered as a fuzzy model, which would give insight into the real system, and provide a method to simplify the neural network .The results show that the performance of this system is very good at learning and predicting.