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 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.