Port Throughput Forecast Model Based on Adam Optimized GRU Neural Network

2020 ◽  
Author(s):  
Xiubin Chen ◽  
Lei Huang
Keyword(s):  
Neural Network ◽  
Forecast Model ◽  
Model Based ◽  
Port Throughput

The Research of Railway Passenger Flow Prediction Model Based on BP Neural Network

2012 ◽  
Vol 605-607 ◽  
pp. 2366-2369 ◽  
Author(s):  
Yao Wang ◽  
Dan Zheng ◽  
Shi Min Luo ◽  
Dong Ming Zhan ◽  
Peng Nie
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Good Effect ◽  
Passenger Flow ◽  
Model Based ◽  
Flow Prediction ◽  
Transport Organization ◽  
Railway Passenger ◽  
Short Time

Based on analyzing the principle of BP neural network and time sequence characteristics of railway passenger flow, the forecast model of railway short-term passenger flow based on BP neural network was established. This paper mainly researches on fluctuation characteristics and short-time forecast of holiday passenger flow. Through analysis of passenger flow and then be used in passenger flow forecasting in order to guide the transport organization program especially the train plan of extra passenger train. And the result shows the forecast model based on BP neural network has a good effect on railway passenger flow prediction.

FA-BP Neural Network-Based Forecast for Railway Passenger Volume

2014 ◽  
Vol 641-642 ◽  
pp. 673-677
Author(s):  
Meng Tian Li ◽  
Xiang Feng Ji ◽  
Jian Zhang ◽  
Bin Ran
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Forecast Model ◽  
Original Data ◽  
Volume Data ◽  
Model Based ◽  
Forecast Models ◽  
Railway Passenger ◽  
Bp Neural Network Model

The research presents a long-term forecast model based on the use of a back-propagation (BP) neural network. Firstly, a brief overview of the forecast models and BP neural network model is demonstrated. Then the improved BP model based on factor analysis (FA-BP) and algorithmfor solving the model are presented. At last, a numerical case study is shown.As the current statistic yearbook only provides the volume data of Jing-Hu corridor, the notion of economical relation intensityis applied to process the original data. The results show that FA-BP neural network is effective in forecast. The proposed model providesa reference in the forefront field of integrated regional transportation planning.

Enterprise Profit Forecast Model Based on Long Short-Term Memory Neural Network

2021 ◽  
Author(s):  
Zhang Qianyu ◽  
Liu Dongping ◽  
Zhu Xueying ◽  
Chen Huaisen ◽  
Zhou Xiaozhou
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Forecast Model ◽  
Short Term ◽  
Term Memory ◽  
Model Based ◽  
Long Short Term Memory
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