| 基于RBF神经网络的交通流预测 |
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| 引用本文： | 秦伟刚,黄琦兰,尹海欣,贾磊. 基于RBF神经网络的交通流预测[J]. 天津工业大学学报, 2006, 25(2): 71-73 |
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| 作者姓名： | 秦伟刚 黄琦兰 尹海欣 贾磊 |
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| 作者单位： | 1. 天津工业大学,计算机技术与自动化学院,天津,300160
2. 山东大学,控制科学与工程学院,济南,250061 |
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| 摘 要： | 针对交通模型是一个非线性、不确定的复杂动力学系统,难以用精确模型来表达的问题,采用RBF神经网络建立交通流预测模型,具有较强的局部泛化能力,收敛速度快,克服了BP神经网络收敛速度慢、易陷入局部极小的缺点．实例仿真研究表明,该方法预测效果较好．
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| 关 键 词： | 交通流 RBF神经网络 预测模型 高斯核函数 |
| 文章编号： | 1671-024X（2006）02-0071-03 |
| 收稿时间： | 2005-09-22 |
| 修稿时间： | 2005-09-22 |
Traffic flow forecasting based on RBF neural network |
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| QIN Wei-gang,HUANG Qi-lan,YIN Hai-xin,JIA Lei. Traffic flow forecasting based on RBF neural network[J]. Journal of Tianjin Polytechnic University, 2006, 25(2): 71-73 |
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| Authors: | QIN Wei-gang HUANG Qi-lan YIN Hai-xin JIA Lei |
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| Affiliation: | 1. School of Computer Technology and Automation, Tianjin Polytechnic University, Tianjin 300160, China; 2. School of Control Science and Engineering, Shandong University, Jinan 250061, China |
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| Abstract: | In the light of the problems of that the traffic model is a nonlinear, uncertain, complex dynamic system and hard to be described completely, the traffic flow forecasting model is successfully constructed by using RBF neural network. The Radial Basis Function neural network with the local generalization abilities and fast convergence speed can overcome the shortcomings of slow convergence speed and local minimum of BP network. The simulation experiment results illuminate that the application of RBF network is fairly effective. |
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| Keywords: | traffic flow RBF neural network forecasting model gaussian kernel function |
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