一种基于模糊神经网络的交通方式划分模型

A Traffic Modal Splitting Model Based on FNN

模糊神经网络具有良好的模式分类能力,而交通方式划分问题本质上也是一个模式分类问题。文章利用模糊神经网络方法对交通方式划分问题进行了建模,通过对FNN和BP神经网络(BPNN)方法运算结果的比较分析,证实模糊神经网络具有收敛速度快、精度高等优点,是模式划分问题的一种实用方法。

Fuzzy neural network（FNN）is the outcome of the combination of neural network and fuzzy logic system. Therefore, it has the strong points of the both, which enables FNN to have the functions of learning, connection, self-adaptation and fuzzy thinking. FNN has the good ability in modal splitting. The problem of traffic modal splitting is also a modal splitting question in essence. In this article, a model of traffic modal splitting is constructed based on FNN method. Through comparing the operation result of FNN with that of BPNN, it is confirmed that FNN has high speed of convergence and high precision. It is a practical method of modal splitting.