A network traffic forecasting method based on SA optimized ARIMA–BP neural network

2021 ◽  
Vol 193 ◽  
pp. 108102
Author(s):  
Hanyu Yang ◽  
Xutao Li ◽  
Wenhao Qiang ◽  
Yuhan Zhao ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Network Traffic ◽  
Traffic Forecasting ◽  
Forecasting Method

A Forecasting Method for Rainfall Distribution at Four Rainfall Stations in Xining Area Based on BP Neural Network

2021 ◽  
pp. 275-292
Author(s):  
Zhuang Xiong ◽  
Jun Ma ◽  
Bingrong Zhou ◽  
Lingfei Zhang ◽  
Bohang Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Rainfall Distribution ◽  
Forecasting Method

A Short-Term Power Load Forecasting Method Based on BP Neural Network

2014 ◽  
Vol 494-495 ◽  
pp. 1647-1650 ◽  
Author(s):  
Ling Juan Li ◽  
Wen Huang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
High Speed ◽  
Load Forecasting ◽  
High Accuracy ◽  
Back Propagation Algorithm ◽  
Short Term ◽  
Power Load ◽  
Forecasting Method ◽  
Power Load Forecasting

Short-term power load forecasting is very important for the electric power market, and the forecasting method should have high accuracy and high speed. A three-layer BP neural network has the ability to approximate any N-dimensional continuous function with arbitrary precision. In this paper, a short-term power load forecasting method based on BP neural network is proposed. This method uses the three-layer neural network with single hidden layer as forecast model. In order to improve the training speed of BP neural network and the forecasting efficiency, this method firstly reduces the factors which affect load forecasting by using rough set theory, then takes the reduced data as input variables of the BP neural network model, and gets the forecast value by using back-propagation algorithm. The forecasting results with real data show that the proposed method has high accuracy and low complexity in short-term power load forecasting.

Research on Wireless Sensor Networks Data Fusion Algorithm Based on COA-BP

2014 ◽  
Vol 539 ◽  
pp. 247-250
Author(s):  
Xiao Xiao Liang ◽  
Li Cao ◽  
Chong Gang Wei ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Fusion ◽  
Network Lifetime ◽  
Bp Neural Network ◽  
Network Traffic ◽  
Bp Algorithm ◽  
Wireless Sensor

To improve the wireless sensor networks data fusion efficiency and reduce network traffic and the energy consumption of sensor networks, combined with chaos optimization algorithm and BP algorithm designed a chaotic BP hybrid algorithm (COA-BP), and establish a WSNs data fusion model. This model overcomes shortcomings of the traditional BP neural network model. Using the optimized BP neural network to efficiently extract WSN data and fusion the features among a small number of original date, then sends the extracted features date to aggregation nodes, thus enhance the efficiency of data fusion and prolong the network lifetime. Simulation results show that, compared with LEACH algorithm, BP neural network and PSO-BP algorithm, this algorithm can effectively reduce network traffic, reducing 19% of the total energy consumption of nodes and prolong the network lifetime.

A case study on a hybrid wind speed forecasting method using BP neural network

2011 ◽  
Vol 24 (7) ◽  
pp. 1048-1056 ◽  
Author(s):  
Zhen-hai Guo ◽  
Jie Wu ◽  
Hai-yan Lu ◽  
Jian-zhou Wang
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Bp Neural Network ◽  
Wind Speed Forecasting ◽  
Forecasting Method

scholarly journals Prediction of Network Traffic of Smart Cities Based on DE-BP Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 55807-55816 ◽  
Author(s):  
Xiuqin Pan ◽  
Wangsheng Zhou ◽  
Yong Lu ◽  
Na Sun
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Network Traffic ◽  
Smart Cities
Sign in / Sign up

Export Citation Format

Share Document