scholarly journals Transient Power Quality Recognition Based on BP Neural Network Theory

2012 ◽  
Vol 16 ◽  
pp. 1386-1392 ◽  
Author(s):  
Xu Tongyu ◽  
Zheng wei ◽  
Sun Peng ◽  
Zhang Qin
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Bp Neural Network ◽  
Network Theory ◽  
Neural Network Theory ◽  
Transient Power Quality

scholarly journals Research on Danjiang Water Quality Prediction Based on Improved Artificial Bee Colony Algorithm and Optimized BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jian’qiang He ◽  
Naian Liu ◽  
Mei’lin Han ◽  
Yao Chen
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Abc Algorithm ◽  
Bee Colony

In order to ensure “a river of clear water is supplied to Beijing and Tianjin” and improve the water quality prediction accuracy of the Danjiang water source, while avoiding the local optimum and premature maturity of the artificial bee colony algorithm, an improved artificial bee colony algorithm (ABC algorithm) is proposed to optimize the Danjiang water quality prediction model of BP neural network is proposed. This method improves the local and global search capabilities of the ABC algorithm by adding adaptive local search factors and mutation factors, improves the performance of local search, and avoids local optimal conditions. The improved ABC algorithm is used to optimize the weights and thresholds of the BP neural network to establish a water quality grade prediction model. Taking the water quality monitoring data of Danjiang source (Shangzhou section) from 2015 to 2019 as the research object, it is compared with GA-BP, PSO-BP, ABC-BP, and BP models. The research results show that the improved ABC-BP algorithm has the highest prediction accuracy, faster convergence speed, stronger stability, and robustness.

Material procedure quality forecast based on genetic BP neural network

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740080 ◽  
Author(s):  
Bao-Hua Zheng
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
High Accuracy ◽  
Process Quality ◽  
Quality Prediction ◽  
Fast Convergence Rate ◽  
Material Process ◽  
Quality Forecast

Material procedure quality forecast plays an important role in quality control. This paper proposes a prediction model based on genetic algorithm (GA) and back propagation (BP) neural network. It can obtain the initial weights and thresholds of optimized BP neural network with the GA global search ability. A material process quality prediction model with the optimized BP neural network is adopted to predict the error of future process to measure the accuracy of process quality. The results show that the proposed method has the advantages of high accuracy and fast convergence rate compared with BP neural network.

Application of artificial neural networks for prediction of sinter quality based on process parameters control

2019 ◽  
Vol 42 (3) ◽  
pp. 422-429
Author(s):  
Huijun Shao ◽  
Zhengming Yi ◽  
Zhuo Chen ◽  
Zheng Zhou ◽  
Zhidan Deng
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Process Parameters ◽  
Bp Neural Network ◽  
Control Point ◽  
Back Propagation ◽  
Learning Ability ◽  
Quality Prediction ◽  
Sintering Process ◽  
Evaluation Indexes

According to the characteristics of non-linearity, strong coupling and a large time delay in the sintering process, the overall analysis for the sintering process has been carried out from the process parameter control point. The sinter performance evaluation indexes and the main influential parameters were determined. The quality prediction model for the sintering process was established using back propagation (BP) neural network algorithm with momentum and variable learning rate. The simulation experimental results show that the model has a higher prediction accuracy and a stronger self-learning ability. The predictive hit rate of random samples is over 81% by adopting BP neural network with the structure of 15-24-4 and network error is 0.65×10−3, thereby verifying the accuracy and effectiveness of the quality prediction model on the basis of process parameters control.

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