Grinding granularity prediction based on improved particle swarm optimization and gray RBF neural network model

2018 ◽  
Author(s):  
Liu Xuqiang ◽  
Zhang Yong ◽  
Wang Siqi
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Network Model ◽  
Neural Network Model ◽  
Rbf Neural Network ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Improved Particle 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.

Algorithm Researching of RBF Neural Network Based on Improved PSO

2011 ◽  
Vol 179-180 ◽  
pp. 233-238 ◽  
Author(s):  
Hua Chen ◽  
Yi Ren Fan ◽  
Shao Gui Deng
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Rbf Neural Network ◽  
Particle Swarm ◽  
Parameter Vector ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Whole Space ◽  
Best Parameter ◽  
Improved Pso

In view of the defect of particle swarm optimization which easily gets into partial extremum, the paper put out an improved particle swarm optimization, and applies the algorithm to the selecting of parameter of RBF neural network basal function. It searches the best parameter vector in the whole space, according to coding means, iterative formula, adapted function which the paper puts forwards. The experiment proves that RBF neural network based on improved PSO has faster convergent speed, and higher error precision.

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