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Received:September 28, 2014 Revised:October 24, 2014
Received:September 28, 2014 Revised:October 24, 2014
中文摘要: 研究使用GA-FNN算法实时提取车辆监控中运行异常的问题. 利用遗传算法对全局信息的搜索特性, 筛选出有效的传感器信息作为模糊神经网络的输入, 通过模糊神经网络训练出模糊矩阵, 实时检测车辆运行的异常程度. 仿真结果显示GA-FNN算法在车辆实时监控系统中, 可以快速的检测出车辆的异常程度.
Abstract:This paper utilizes the GA-FNN algorithms to extract the unusual problems in real-time vehicle monitoring. GA algorithm has the features of searching the global information, thus in this way a new approach is given to select the effective sensor information, and use it as the input of fuzzy neural network. The fuzzy matrix trained by fuzzy neural network can detect the degree of abnormal through real-time operation of the vehicle. The simulation examples demonstrate the validity of the application of the GA-FNN, the abnormal degree can be detected quickly.
keywords: information fusion genetic algorithm feature dimension reduction fuzzy neural network vehicle-mounted monitoring
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张坡,郝敬彬,王焕,彭淑彦.基于信息融合的车辆运行异常程度检测.计算机系统应用,2015,24(5):267-271
ZHANG Po,HAO Jing-Bin,WANG Huan,PENG Shu-Yan.Detection Abnormal Degree in Real-Time Vehicle Based on Information Fusion.COMPUTER SYSTEMS APPLICATIONS,2015,24(5):267-271
张坡,郝敬彬,王焕,彭淑彦.基于信息融合的车辆运行异常程度检测.计算机系统应用,2015,24(5):267-271
ZHANG Po,HAO Jing-Bin,WANG Huan,PENG Shu-Yan.Detection Abnormal Degree in Real-Time Vehicle Based on Information Fusion.COMPUTER SYSTEMS APPLICATIONS,2015,24(5):267-271
