Short-term traffic flow prediction is an effective means for intelligent transportation system (ITS) to mitigate traffic congestion. However, traffic flow data with temporal features and periodic characteristics are vulnerable to weather effects, making short-term traffic flow prediction a challenging issue. However, the existing models do not consider the influence of weather changes on traffic flow, leading to poor performance under some extreme conditions. In view of the rich features of traffic data and the characteristic of being vulnerable to external weather conditions, the prediction model based on traffic data has certain limitations, so it is necessary to conduct research studies on traffic flow prediction driven by both the traffic data and weather data. This paper proposes a combined framework of stacked autoencoder (SAE) and radial basis function (RBF) neural network to predict traffic flow, which can effectively capture the temporal correlation and periodicity of traffic flow data and disturbance of weather factors. Firstly, SAE is used to process the traffic flow data in multiple time slices to acquire a preliminary prediction. Then, RBF is used to capture the relation between weather disturbance and periodicity of traffic flow so as to gain another prediction. Finally, another RBF is used for the fusion of the above two predictions on decision level, obtaining a reconstructed prediction with higher accuracy. The effectiveness and robustness of the proposed model are verified by experiments.
The factors that affect the ship equipment fault grade assessment are analyzed firstly, and then the fault grade assessment model is founded on the base of back propagation neural network. The genetic algorithm is used to quantify the value of the initial weight vector of neural network. Three methods that are gradient descent back propagation algorithm, momentum gradient descent back propagation algorithm and Levenberg-Marquard back propagation algorithm are used to train the neural network. Through lots of simulation calculation, the neural network simulation algorithm which is most adaptive to this special assessment problem and has the highest precision is found. Next, the methods through which can improve the assessment precision are given. In the end, the visualization forms of the neural network model which are compiled by Matlab and VB software is researched to improve the usability of the methods.
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