计算机工程与应用Issue(19)：242-245,4.DOI:10.3778/j.issn.1002-8331.1112-0613
基于改进模糊聚类与ANFIS的高速公路事件检测
Freeway incident detection based on improved fuzzy clustering arithmetic and ANFIS
摘要
Abstract
In order to accurately and timely detect highway traffic accident, reduce traffic delay and improve highway safety, this paper combines subtractive clustering and Fuzzy C-Means(FCM) clustering method to cluster the input sample data to build the initial fuzzy inference system, then the hybrid algorithm is used to train the parameters of the fuzzy system, determine the fuzzy reasoning rules, and establish a final training fuzzy model. Compared with the simulation experimental results, the method obtains excellent performance on ROC(Receiver Operation Characteristic)curve, shows the validity of the modeling method based on the improved fuzzy clustering and Adaptive Neural Fuzzy Inference System(ANFIS).关键词
交通事件检测/模糊C均值聚类/减法聚类/自适应神经模糊推理/ROC曲线Key words
freeway incident detection/Fuzzy C-Means(FCM)clustering/subtractive clustering/Adaptive Neural Fuzzy Infer-ence/ROC curve分类
信息技术与安全科学引用本文复制引用
姚磊,刘渊..基于改进模糊聚类与ANFIS的高速公路事件检测[J].计算机工程与应用,2013,(19):242-245,4.基金项目
国家自然科学基金（No.61103223）；江苏省自然科学基金重点研究专项（No.BK2011003）。 （）
