scholarly journals Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm

2017 ◽  
Vol 6 (6) ◽  
pp. 154
Author(s):  
Mengshan Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Petroleum Price ◽  
Artificial Neural ◽  
Swarm Algorithm ◽  
Self Adaptive

scholarly journals Modeling and optimizing an electrochemical oxidation process using artificial neural network, genetic algorithm and particle swarm optimization

2018 ◽  
Vol 83 (3) ◽  
pp. 379-390
Author(s):  
Banghai Liu ◽  
Chunji Jin ◽  
Jiteng Wan ◽  
Pengfang Li ◽  
Huanxi Yan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Electrochemical Oxidation ◽  
Back Propagation ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Operation Conditions ◽  
Artificial Neural

This study proposes a novel hybrid of artificial neural network (ANN), genetic algorithm (GA), and particle swarm optimization (PSO) to model and optimize the relevant parameters of an electrochemical oxidation (EO) Acid Black 2 process. The back propagation neural network (BPNN) was used as a modelling tool. To avoid over-fitting, GA was applied to improve the generalized capability of BPNN by optimizing the weights. In addition, an optimization model was developed to assess the performance of the EO process, where total organic carbon (TOC) removal, mineralization current efficiency (MCE), and the energy consumption per unit of TOC (ECTOC) were considered. The operation conditions of EO were further optimized via PSO. The validation results indicted the proposed method to be a promising method to estimate the efficiency and to optimize the parameters of the EO process.

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.

Test on Flood Prediction-Model Using Artificial Neural Network for ShiiLiAn Hydrologic Station on MinChiang,China

2010 ◽  
Vol 39 ◽  
pp. 555-561 ◽  
Author(s):  
Qing Hua Luan ◽  
Yao Cheng ◽  
Zha Xin Ima
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flood Control ◽  
Back Propagation ◽  
Forecast Model ◽  
Flood Forecast ◽  
Back Propagation Algorithm ◽  
Nonlinear Method ◽  
Flood Prediction ◽  
Artificial Neural

The establishing of a precise simulation model for runoff prediction in river with several tributaries is the difficulty of flood forecast, which is also one of the difficulties in hydrologic research. Due to the theory of Artificial Neural Network, using Back Propagation algorithm, the flood forecast model for ShiLiAn hydrologic station in Minjiang River is constructed and validated in this study. Through test, the result shows that the forecast accuracy is satisfied for all check standards of flood forecast and then proves the feasibility of using nonlinear method for flood forecast. This study provides a new method and reference for flood control and water resources management in the local region.

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