Abstract
In recent years, the traffic volume of the Yangtze River has increased dramatically. In order to provide more favorable assistance to port planning and traffic management, the accuracy of port ship traffic volume prediction is very important. In this paper, genetic algorithm and wavelet analysis and neural network are used to construct the genetic wavelet neural network model prediction model, and BP neural network prediction model is established. The ship volume of Jiujiang Port is used as experimental data to simulate and analyze. The results show that the prediction accuracy of the genetic wavelet neural network prediction model is significantly higher than that of the BP neural network prediction model. It is proved that the genetic wavelet neural network has broad application prospects for ship traffic flow prediction in the Yangtze River port. This method has practical application significance.
Highlights
Wavelet neural networkWavelet neural network is a breakthrough by wavelet analysis in recent years, research on the basis of a kind of artificial neural network is put forward, which is based on the theory of wavelet analysis and wavelet transform is a kind of hierarchical structure made by new type of multiresolution and artificial neural network model, which USES nonlinear wavelet base instead of the usual nonlinear sigmoid function, the signal is described as table by the selection of wavelet base linear superposition, the corresponding input layer to hidden layer weights and threshold of hidden layer, respectively, by the expansion of the scale of the wavelet function is replaced by the translation factor and time factor
These In recent years, the domestic economic development trend has become better and better, and various industries have developed rapidly
In order to meet the needs of economic growth, the traffic volume of the Yangtze River has increased sharply in recent years, and the development of China's waterway transportation is prominent at this stage
Summary
Wavelet neural network is a breakthrough by wavelet analysis in recent years, research on the basis of a kind of artificial neural network is put forward, which is based on the theory of wavelet analysis and wavelet transform is a kind of hierarchical structure made by new type of multiresolution and artificial neural network model, which USES nonlinear wavelet base instead of the usual nonlinear sigmoid function, the signal is described as table by the selection of wavelet base linear superposition, the corresponding input layer to hidden layer weights and threshold of hidden layer, respectively, by the expansion of the scale of the wavelet function is replaced by the translation factor and time factor. Wavelet transform is a time-frequency localization analysis method whose window size is fixed but whose shape, time window and frequency window can be changedm[6,7]. Its basic principle is a layered, multiresolution new artificial neural network model composed of wavelet analysis and wavelet transform. By optimizing the connection weight between layers, the sum of squared errors between the actual output value of the neural network and the ideal output is minimized. The genetic algorithm is simple to operate, and its parallel search ability is integrated into the nerve. When genetic algorithm is applied to the optimization of network weights, its implicit parallel performance can overcome the difficulty that weights are trapped in local minima in the process of training, and it has the powerful function mapping and approximation ability of neural networks [10]
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