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Automatic freeway incident detection system and method using artificial neural network and genetic algorithms

机译:利用人工神经网络和遗传算法的高速公路自动事件检测系统及方法

摘要

Design of a neural network for automatic detection of incidents on a freeway is described. A neural network is trained using a combination of both back-propagation and genetic algorithm-based methods for optimizing the design of the neural network. The back-propagation and genetic algorithm work together in a collaborative manner in the neural network design. The training starts with incremental learning based on the instantaneous error and the global total error is accumulated for batch updating at the end of the training data being presented to the neural network. The genetic algorithm directly evaluates the performance of multiple sets of neural networks in parallel and then use the analyzed results to breed new neural networks that tend to be better suited to the problems at hand.
机译:描述了用于自动检测高速公路事故的神经网络的设计。使用反向传播和基于遗传算法的方法的组合来训练神经网络,以优化神经网络的设计。反向传播和遗传算法在神经网络设计中以协作的方式协同工作。训练从基于瞬时误差的增量学习开始,并且在将训练数据提供给神经网络的末尾累积全局总误差以进行批更新。遗传算法直接并行评估多组神经网络的性能,然后使用分析结果来培育新的神经网络,这些神经网络往往更适合当前的问题。

著录项

  • 公开/公告号US6470261B1

    专利类型

  • 公开/公告日2002-10-22

    原文格式PDF

  • 申请/专利权人 CET TECHNOLOGIES PTE LTD;

    申请/专利号US20010743992

  • 发明设计人 YEW LIAM NG;KIM CHWEE NG;

    申请日2001-01-16

  • 分类号G06G77/60;G06N30/20;

  • 国家 US

  • 入库时间 2022-08-22 00:48:33

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