研究了采用模糊神经网络来识别 JL UIV- 2型视觉导航智能车辆模糊和脏污的导航路径的方法，提出了两种模糊神经网络模型.第 1种模糊神经网络有 5层结构，采用正态分布概率函数作为模糊化函数 ;第 2种模糊神经网络有 6层结构，采用 π函数作为模糊化函数.同时采用改进的快速 BP算法对这两种模糊神经网络进行训练，并采用实际模糊和脏污的条带状导航路标图象进行了识别试验.试验结果表明，所提出的模糊神经网络可使智能车辆有效地识别出模糊和脏污的导航路径

Intelligent vehicle can automatic drive, so the drive fatigue can be avoided totally and drive safety can be improved markedly. The research of intelligent vehicle is important aspect of intelligent transportation system. In order to ensure reliable navigating, the navigation mark should keep clean and clear. When the navigation mark becomes blur and smudges, the correct rate of the mark recognition descends, and the navigation reliability of intelligent vehicle also descends. In order to settle the problem, the method of recognizing blur and smudge navigation lane is studied by using fuzzy neural network for JLUIV-2 vision navigation intelligent vehicle. Two fuzzy neural network models are developed. One model is made up of 5 layers, its fuzzification function is a normal distribution probability function, another model has 6 layers, and its fuzzification function isπfunction. The modified quick BP algorithm is used to train the two fuzzy neural networks. Practical recognizing experiments are made by using image of blur and smudge stripe navigation mark. The results show the two fuzzy neural networks can effectively recognize the blur and smudge lane of JLUIV-2 intelligent vehicle. In order to satisfy the real-time requirement, a 10×300 interesting area abstracted form 222×300 image is processed in navigation.