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

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.

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

2018 Chinese Control And Decision Conference (CCDC) ◽

10.1109/ccdc.2018.8407247 ◽

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

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Particle Swarm optimization based Neural Network Model for Chaotic Time Series Forecasting

2020 12th International Conference on Advanced Computational Intelligence (ICACI) ◽

10.1109/icaci49185.2020.9177737 ◽

2020 ◽

Author(s):

Xin Li ◽

Weijie Ren ◽

Jingying Zhao ◽

Min Han

Keyword(s):

Neural Network ◽

Time Series ◽

Particle Swarm Optimization ◽

Network Model ◽

Neural Network Model ◽

Particle Swarm ◽

Time Series Forecasting ◽

Chaotic Time Series ◽

Swarm Optimization

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-179802 ◽

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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Training Back-Propagation Neural Network for Target Localization Using Improved Particle Swarm Optimization

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.333-335.1384 ◽

2013 ◽

Vol 333-335 ◽

pp. 1384-1387

Author(s):

Jin Jie Yao ◽

Xiang Ju ◽

Li Ming Wang ◽

Jin Xiao Pan ◽

Yan Han

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Back Propagation ◽

Particle Swarm ◽

Back Propagation Neural Network ◽

Position Estimation ◽

Target Localization ◽

Prediction Ability ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization

Target localization technology has been intensively studied and broadly applied in many fields. This paper presents one improved particle swarm optimization technique in training a back-propagation neural network for position estimation in target localization. The proposed scheme combines particle swarm optimization (PSO), back-propagation neural network (BP), adaptive inertia weight and hybrid mutation, called IPSO-BP. To verify the proposed IPSO-BP approach, comparisons between the PSO-based BP approach (PSO-BP) and general back-propagation neural network (BP) are made. The computational results show that the proposed IPSO-BP approach exhibits much better performance in the training process and better prediction ability in the validation process than those using the other two base line approaches.

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