Wavelet neural network with improved genetic algorithm for traffic flow time series prediction

In the traditional wavelet neural network (WNN) prediction model, the parameter optimization is performed using a unidirectional gradient descent algorithm, which has the problems of slow convergence and local optimum. To improve the predication accuracy of short-term traffic flow, a predication mod...

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Bibliographic Details
Published in:Optik (Stuttgart) 2016-10, Vol.127 (19), p.8103-8110
Main Authors: Yang, Hong-jun, Hu, Xu
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Genetic algorithms
Improved genetic algorithm
Mathematical models
Neural networks
Optimization
Parameter optimization
Traffic flow
Traffic flow forecast
Wavelet
Wavelet neural network
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Summary:In the traditional wavelet neural network (WNN) prediction model, the parameter optimization is performed using a unidirectional gradient descent algorithm, which has the problems of slow convergence and local optimum. To improve the predication accuracy of short-term traffic flow, a predication model based on clustering search strategy improved genetic algorithm (IGA) and WNN (IGA-WNN) is proposed. The IGA is used to optimize the initial connection weights, translation factor and scaling factor of WNN. The algorithm is applied to the short-term traffic flow of empirical research. The experimental results show that IGA-WNN model has a higher predication accuracy and a better nonlinear fitting ability compared with the traditional WNN and GA-WNN prediction models.
ISSN:0030-4026
1618-1336
DOI:10.1016/j.ijleo.2016.06.017