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Neural networks and fuzzy data fusion. Application to an online and real time vehicle detection system

机译:神经网络和模糊数据融合。在在线实时车辆检测系统中的应用

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摘要

In this article we are presenting an on-line and real-time vehicle detection system. This system detects vehicles passing over magnetic sensors. It works independently of their initial position and of strong magnetic disturbance possibly induced by the load carried on the vehicles. This system is based on the co-operation of reflective agents, using a reliability measure of their outputs. The fusion of the data delivered by each agent is obtained through fuzzy logic rules. The system is also strengthened to resist substantial magnetic disturbance (even non-periodic ones), it uses the three components of the magnetic field, and is rotation-invariant. Furthermore, its modular design opens up many possibilities of evolution.
机译:在本文中,我们提出了一种在线实时车辆检测系统。该系统检测经过磁传感器的车辆。它的工作与其初始位置无关,并且不受车辆负载可能引起的强烈电磁干扰的影响。该系统基于反射剂的合作,使用反射剂输出的可靠性度量。每个代理传递的数据的融合是通过模糊逻辑规则获得的。该系统也得到了增强,可以抵抗大量的电磁干扰(甚至是非周期性的),它利用了磁场的三个分量,并且旋转不变。此外,其模块化设计为发展提供了许多可能性。

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