scholarly journals A Hybrid Model Based on Back-Propagation Neural Network and Optimized Support Vector Machine with Particle Swarm Algorithm for Assessing Blade Icing on Wind Turbines

2021 ◽  
Vol 118 (6) ◽  
pp. 1869-1886
Author(s):  
Xiyang Li ◽  
Bin Cheng ◽  
Hui Zhang ◽  
Xianghan Zhang ◽  
Zhi Yun
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Hybrid Model ◽  
Wind Turbines ◽  
Back Propagation ◽  
Particle Swarm ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Swarm Algorithm

scholarly journals A Novel Back Propagation Neural Network Optimized by Rough Set and Particle Swarm Algorithm for Remanufacturing Service Provider Classification and Selection

2021 ◽  
Vol 2083 (4) ◽  
pp. 042058
Author(s):  
Jianhua Cao ◽  
Xuhui Xia ◽  
Lei Wang ◽  
Xiang Liu ◽  
Zelin Zhang
Keyword(s):  
Neural Network ◽  
Rough Set ◽  
Service Provider ◽  
Service Providers ◽  
Back Propagation ◽  
Particle Swarm ◽  
Back Propagation Neural Network ◽  
Particle Swarm Algorithm ◽  
Initial Weight ◽  
Swarm Algorithm

Abstract Aiming at the problem that the high classification feature dimensionality of the back propagation neural network (BPNN) leads to slow convergence speed and the initial weight and threshold sensitivity of the BPNN lead to the problem of easy convergence to the local optimum. A novel BPNN optimized by rough set and particle swarm algorithm (RS-PSO-BPNN) for remanufacturing service provider classification and selection is proposed. First, the attribute reduction method of rough set theory is used to preprocess the classification features of remanufacturing service providers, redundant attributes are deleted from the decision table, and the input feature dimension is reduced; then the PSO algorithm is used to optimize the network Initial weight and threshold. Finally, the proposed method is used for the selection and optimization of remanufacturing service providers. The results show that the proposed RS-PSO-BPNN has higher classification accuracy and efficiency for the problem, which provides scientific decision supports for remanufacturing service provider selection.

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