基于改进的模糊C均值的BP分类器设计

提出了一种基于改进模糊C均值的BP神经网络分类器的设计,通过改进的模糊C均值算法对大量的数据进行聚类划分,然后设计BP神经网络对划分后的数据进行训练和测试,最后由计算机进行综合判断.试验证明该分类器是有效的,可以对高速公路车辆的车型进行迅速判别.

A design of BP neural networks classifier based on modified fuzzy C-means clustering is presented, with which the mas-sive data was classified by modified fuzzy C-means clustering algorithm firstly, and then design the BP neural networks to train and test the classified data. Finally it was carried on compressive judgment by the computer. Experiments prove the validity of the classi-fier; it can recognize the highway vehicle types rapidly.

辽宁省自然科学基金资助项目项目名称:汽车行业电子物流模式及动态优化控制方法研究(编号:20042005)