scholarly journals Image Forgery Detection Using Back Propagation Neural Network Model and Particle Swarm Optimization Algorithm

2020 ◽  
Vol 3 (1) ◽  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Network Model ◽  
Neural Network Model ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Forgery Detection ◽  
Swarm Optimization

scholarly journals A Short-Term Forecast Model of foF2 Based on Elman Neural Network

2019 ◽  
Vol 9 (14) ◽  
pp. 2782
Author(s):  
Jieqing Fan ◽  
Chao Liu ◽  
Yajing Lv ◽  
Jing Han ◽  
Jian Wang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Particle Swarm ◽  
Elman Neural Network ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Single Station

The critical frequency foF2 of the ionosphere F2 layer is one of the most important parameters of the ionosphere. Based on the Elman neural network (ENN), this paper constructs a single station forecasting model to predict foF2 one hour ahead. In order to avoid the network falling into local minimum, the model is optimized by the improved particle swarm optimization (IPSO). The input parameters used in the model include local time, seasonal information, solar cycle information and magnetic activity information. Data of the Wuhan Station from 2008 to 2016 were used to train and test the model. The prediction results of foF2 show that the root mean square error (RMSE) of the Elman neural network model is 4.30% lower than that of the back-propagation neural network (BPNN) model. The RMSE is further reduced by 8.92% after using the IPSO to optimize the model. This indicates that the Elman neural network model optimized by the improved particle swarm optimization is superior to the BP neural network and Elman neural network in the forecast of foF2 one hour ahead at Wuhan station.

Research on neural network model for new energy industry economy based on particle swarm optimization

2020 ◽  
Vol 38 (6) ◽  
pp. 7267-7277
Author(s):  
Xuhua Kang ◽  
Yuzheng Zhang ◽  
Huwei Zhang ◽  
Shenzhao Li ◽  
Wenjing Gao
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Network Model ◽  
Neural Network Model ◽  
Particle Swarm ◽  
Energy Industry ◽  
Swarm Optimization ◽  
New Energy
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