Abstract: This paper presents a new hybrid intelligence algorithm for automatically detecting ft-way incidents, which employs fuzzy clustering and RBF neural computing technique. An improved OLS (Orthogonal Least Squares) selection algorithm for training RBF neural networks is also proposed. The simulation results illustrate that the improved OLS selection algorithm accelerates the training of the RBF neural networks substantially and it need not to decide the number of RBF centers in advance. The satisfactory performance can be achieved by using this algorithm in freeway incidents detection.

Key words: Freeway incidents detection, fuzzy clustering, RBF neural networks, OLS

摘要： 提出了一种基于模糊聚类技术和RBF神经网络的混合智能高速公路事件自动探测算法，同时改进了用于RBF神经网络训练的Oils(正交最小二乘)选择算法.仿真实验证明，改进的OLS选择算法大大提高了RBF神经网络的训练速度同时具有无须事先确定RBF中心的优点，将之运用于公路事件探测可以获得满意的性能.

关键词: 高速公路事件探测, 模糊聚类, RBF神经网络, 正交最小二乘算法