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A prediction Model of the Number of Taxicabs Based on Wavelet Neural Network

机译:基于小波神经网络的出租车数量的预测模型

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Wavelet neural network is superior to traditional neural networks in the fact that the wavelet functions have good time-frequency localization characteristics and fast decay. To improve the prediction accuracy of the number of taxicabs, in this paper, wavelet neural network was used to approximate the nonlinearity between taxi number and its influence factors. And we also apply wavelet neural network to model each influence factor to obtain their future values. Then inputting the predictive values of the influence factors to the model of taxicab number, the number of taxicabs in the future will be achieved. Simulations show that the wavelet neural network model of the number of taxicabs has higher prediction accuracy than the BP neural network model.
机译:小波神经网络优于传统的神经网络,因为小波函数具有良好的时频定位特性和快速衰减。为了提高出租车数量的预测准确性,在本文中,采用小波神经网络近似出租车数与其影响因素之间的非线性。我们还将小波神经网络应用于模拟每个影响因素以获得其未来的价值。然后将影响因素的预测值输入到出租车号码的模型中,将来将来的出租车数量。仿真表明,出租车数量的小波神经网络模型具有比BP神经网络模型更高的预测准确性。

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