| 事件检测概率神经网络模型的建立与验证 |
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| 引用本文： | 覃频频[,].事件检测概率神经网络模型的建立与验证[J].ITS通讯,2006,8(1):16-19. |
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| 作者姓名： | 覃频频[ ] |
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| 作者单位： | 广西大学机械工程学院 广西，南宁 530004 西南交通大学交通运输学院560信箱 |
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| 摘 要： | 在对概率神经网络(PNN)的分类机理、输入向量选取和网络设置进行分析的基础上,建立了用于识别两类事件模式(无事件模式和有事件模式)的事件检测PNN模型。采用高速公路路段1-880实地线圈数据集和事件数据集验证模型,通过比较PNN模型与多层前向神经网络(MLF)模型的结果,发现无论对于向北、向南或混合方向的高速公路事件检测,PNN模型的检测率(DR)比MLF模型高;平均检测时间(MTTD)比MLF模型短:但误报率(FAR)也较高。概率神经网络是高速公路事件检测的一种有效算法,其在理论基础、算法和学习速度等方面比多层前向神经网络具有优势。
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| 关 键 词： | 事件检测 概率神经网络 多层前向神经网络 |
Development and simulation of PNN model for incident detection |
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| Authors: | Qin Pinpin |
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| Institution: | 1 College of Transportation, Southwest Jiaotong University, Sichuan Chengdu, 610031, China; 2 College of Mechanical Engineering, Guangxi University, Guangxi Nanning, 530005, China |
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| Abstract: | This paper investigates the classification, input variables and settings of probabilistic neural network, including model development and simulation by I-880 database. Comparison of PNN and MLF simulation results show that DR and MTTD is achieved by PNN are better than MLF, FAR is inferior than MLF whether in north-toward, south-toward and two direction freeway incident detection. PNN is an effective algorithm in incident detection and is superior to MLF in theory, training numbers and learning speed. |
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| Keywords: | Incident Detection PNN(probabilistic neural network) MLF(multi-layer feed-forward neural networks) |
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