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Driver pre-accident behavior pattern recognition based on dynamic radial basis function neural network

机译:基于动态径向基函数神经网络的驾驶员前意外行为模式识别

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In this article, driver's pre-accident behavior mode is systematically studied by means of combining the use of regional mapping error function with conditions of the resource distribution network and utilizing dynamic radial primary function neural network and its training method for pattern recognition. As is proved in this study, this method can not only improve the velocity of network training, reduce network structure, but also improve the properties of network generalization and the precision rate of pattern recognition. Simulated result preferably coincides with the measured result, which improves the adoptive method and the established model in this study to be right.
机译:在本文中,通过将区域映射误差功能与资源分配网络的条件相结合并利用动态径向主要函数神经网络及其模式识别的培训方法来系统地研究了驾驶员的前所未知行为模式。 如本研究所知,这种方法不仅可以提高网络训练的速度,还可以降低网络结构,还可以提高网络泛化的特性和模式识别的精度识别。 模拟结果优选地与测量结果一致,这改善了本研究中的养方法和建立的模型是正确的。

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