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Urban Traffic Controller Using Fuzzy Neural Network and MultiSensors Data Fusion

机译:模糊神经网络和多传感器数据融合的城市交通控制器

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There are massive noises and uncertainty factor in traffic data. It is difficult to obtain accurate data from one sensor. Three layers structure to realize the traffic data acquisition based on multi sensors data fusion is given in this article. In the first layer, data from different data source is integrated to an uniform data stream; In the second layer, fuzzy neural network is used to predict the queue length and traffic time. In the third layer, multiphase fuzzy controller for intersection based on queue length is designed. Feasibility of such method is proved by the simulation of one typical intersection.
机译:交通数据中存在大量的噪声和不确定性因素。从一个传感器获得准确的数据是困难的。给出了基于多传感器数据融合的交通数据采集的三层结构。在第一层中,将来自不同数据源的数据集成到一个统一的数据流中;在第二层中,使用模糊神经网络预测队列长度和交通时间。在第三层中,设计了基于队列长度的交叉口多相模糊控制器。通过对一个典型路口的仿真证明了该方法的可行性。

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