关键词: 分数阶神经网络, 最优阶次, 分数阶傅里叶变换, 交通流量动态预测

Abstract: A forecasting model of optimal order fractional neural network is proposed. Data pre-processing, optimal order optimization, prediction algorithm process step, and performance of predict accuracy are given. More flexible and effective than the BP neural network in function approximation ability, fractional neural network analyzes the data in two ways, time and frequency. Fractional neural network?is similar?to wavelet neural network with the good partial characteristic and distinguish rate, especially for short-term data, and has stronger self adaptation ability, faster convergence rate and higher forecast accuracy. Simulated with the short-term traffic flow, compared with based on wavelet neural network and BP neural network, performance?index?is analyzed and?evaluated. The resolution shows that traffic flow dynamic forecast based on optimal order fractional neural network is realized more flexibly, rapidly and effectively.

Key words: fractional neural network, optimal order, fractional Fourier transform, traffic flow dynamic forecast