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| Genetic Gradient Algorithm Based RBF Neural Network and Its Applications to Traffic Information Prediction |
| GUO Lin1,2,3, FANG TingJian1,2,3, YE JiaSheng3 |
1.Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031
2.Department of Automation, University of Science and Technology of China, Hefei 230027
3.The Research Center for Software Engineering of Anhui Province, Hefei 230088 |
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Abstract The realtime and accurate predicted traffic information is critical to the intelligent traffic inducement and the traffic management. A twostep learning algorithm GGA (Genetic Gradient Algorithm) for radial basis function (RBF) neural network is proposed in this paper. A genetic algorithm (GA) initially determines the parameters of the RBF network including the number and locations of the selected centers and the widths of Gaussian kernel functions in the hidden layer. Then a gradient descent algorithm is adopted to further adjust these parameters of the RBF network. A smaller network with better generalization capability can thus be obtained. The experimental results of the realtime traffic information prediction in Ningbo city show good performance of the proposed method.
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Received: 28 July 2005
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2006, Vol. 19 
