关键词: 全局K-均值聚类, 概率神经网络, 主成分分析, 神经网络集成

Abstract: Aiming at the features of urban traffic state having many influencing factors and difficulty to distinguish，by analyzing K-means clustering algorithm and Probability Neural Network（PNN），and using complementary of multiple sources detection information，this paper proposes probability neural network ensemble model based on rapid global clustering technique to improve diversity of the neural networks ensemble.While using Principal Component Analysis（PCA） to optimize the PNN structure.The simulation results show that the method can use more simple network structure，quickly and efficiently distinguish the urban traffic state than traditional integration methods Bagging，be able to provide data reference for future urban traffic warning and induction.

Key words: global K-mean clustering, Probability Neural Network（PNN）, Principal Component Analysis（PCA）, neural network ensemble