scholarly journals Carbon Emissions Scenario Prediction of the Thermal Power Industry in the Beijing-Tianjin-Hebei Region Based on a Back Propagation Neural Network Optimized by an Improved Particle Swarm Optimization Algorithm

2017 ◽  
Vol 26 (4) ◽  
pp. 1895-1904 ◽  
Author(s):  
Jianguo Zhou ◽  
Shijuan Du ◽  
Jianfeng Shi ◽  
Fengtao Guang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Thermal Power ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Swarm Optimization ◽  
Scenario Prediction ◽  
Emissions Scenario

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

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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