Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting

2014 ◽  
Vol 56 ◽  
pp. 226-239 ◽  
Author(s):  
Chao Ren ◽  
Ning An ◽  
Jianzhou Wang ◽  
Lian Li ◽  
Bin Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Wind Speed ◽  
Bp Neural Network ◽  
Optimal Parameters ◽  
Swarm Optimization ◽  
Wind Speed Forecasting ◽  
Parameters Selection ◽  
Selection For

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

Predict Hypotension Events during Spinal Anesthesia Based on Particle Swarm Optimization Neural Networks

2012 ◽  
Vol 569 ◽  
pp. 733-736
Author(s):  
Hai Jun Dai ◽  
Yu Qiu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
General Anesthesia ◽  
Bp Neural Network ◽  
Simulation Experiment ◽  
Particle Swarm ◽  
Optimal Parameters ◽  
Swarm Optimization ◽  
Accuracy Requirement

Neural network model based on particle swarm optimization (PSO) was established for predicting hypotension during general anesthesia. The BP neural network parameters optimized by pso, and learning samples are trained and modeled by BP neural network with optimal parameters. The simulation experiment is carried out with MATLAB. The result indicated that the model forecasting results are close with the actual results and meet the accuracy requirement to General Anesthesia.

scholarly journals An Improved Method for NURBS Surface Based on Particle Swarm Optimization BP Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 184656-184663
Author(s):  
Xiaoqiang Tian ◽  
Lingfu Kong ◽  
Deming Kong ◽  
Li Yuan ◽  
Dehan Kong
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Nurbs Surface ◽  
Improved Method ◽  
Swarm Optimization

Study on Wind Speed Forecasting Based on STC and BP Neural Network

2013 ◽  
Vol 724-725 ◽  
pp. 623-629
Author(s):  
Xing Jie Liu ◽  
Wen Shu Zheng ◽  
Tian Yun Cen
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Wind Farm ◽  
Target Prediction ◽  
Temporal Correlation ◽  
Economic Security ◽  
Wind Speed Forecasting ◽  
Spatio Temporal

Accurate wind speed forecasting of wind farm is of great significance in economic security and stability of the grid. In order to improve the prediction accuracy, the paper first proposed a spatio-temporal correlation predictor method. Based on physical characteristics of wind speed evolution, the method looked for the wind speed and direction information at sites close to the target prediction site, and established STCP model to forecast. And then we established the BP neural network to finish multi-step forecast with wind speed time series of target forecast site .Last, two methods were combined to form STCP-BP method. Simulation tests are conducted with operation data from certain wind farm group in China and results show that STCP-BP method can effectively improve the prediction accuracy compared with BP model.

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