Failure prediction method of gearbox based on BP neural network with genetic optimization algorithm

2015 ◽  
Author(s):  
Rui Jiang ◽  
Zhuang Li ◽  
Yufeng Ma ◽  
Chao Liu ◽  
Xiaolong Zhang
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Failure Prediction ◽  
Prediction Method ◽  
Genetic Optimization ◽  
Genetic Optimization Algorithm

Prediction for Greenhouse Melon Disease Based on Optimized Neural Network

2013 ◽  
Vol 765-767 ◽  
pp. 1109-1112
Author(s):  
Xiao Hua Sun ◽  
Xi Wang Wang ◽  
Fu Shun Wang ◽  
Yan Zhao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Local Minimum ◽  
Convergence Speed ◽  
Forecasting Model ◽  
Genetic Optimization ◽  
Optimal Solutions ◽  
Global Optimal ◽  
Thick Skin ◽  
Genetic Optimization Algorithm

in the greenhouse, the the thick skin melon tends to be more easily infected by some diseases. The traditional forecasting model disease convergence speed is slow and limited by the minimum, easily. This study, based on the BP neural network but to optimize it and introduce the genetic algorithm, through the local searching nearby the global optimal solutions, overcomes the local minimum value and convergence speed defects of traditional neural network with genetic algorithms global search ability. The experimental datas simulative analysis by Matlab shows that the thick skin melon's disease predicting error has been reduced significantly after the introduction of genetic optimization algorithm and has obtained an ideal fitting result.

Application of Improved BP Network in Failure Forecasting

2012 ◽  
Vol 490-495 ◽  
pp. 373-377
Author(s):  
Zhi Gang Li ◽  
Bo Wei Shi
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Predictive Power ◽  
Failure Prediction ◽  
Prediction Method ◽  
Equipment Failure ◽  
Bp Network ◽  
Neural Network Prediction ◽  
Neural Network Algorithm ◽  
Network Prediction

An improved BP neural network prediction method is used for collecting pipe equipment failure prediction and comparing with the improved BP neural network in front, which demonstrates that the improved BP neural network algorithm to the collecting pipe failures has better predictive power.

scholarly journals Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li
Keyword(s):  
Neural Network ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Evidence Theory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Fault Classification ◽  
Bearing Fault ◽  
Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

scholarly journals An image denoising method based on BP neural network optimized by improved whale optimization algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chunzhi Wang ◽  
Min Li ◽  
Ruoxi Wang ◽  
Han Yu ◽  
Shuping Wang
Keyword(s):  
Neural Network ◽  
Image Denoising ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Weight Coefficient ◽  
Whale Optimization Algorithm ◽  
Denoising Method ◽  
Training Time ◽  
Whale Optimization ◽  
City Construction

AbstractAs an important part of smart city construction, traffic image denoising has been studied widely. Image denoising technique can enhance the performance of segmentation and recognition model and improve the accuracy of segmentation and recognition results. However, due to the different types of noise and the degree of noise pollution, the traditional image denoising methods generally have some problems, such as blurred edges and details, loss of image information. This paper presents an image denoising method based on BP neural network optimized by improved whale optimization algorithm. Firstly, the nonlinear convergence factor and adaptive weight coefficient are introduced into the algorithm to improve the optimization ability and convergence characteristics of the standard whale optimization algorithm. Then, the improved whale optimization algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the dependence in the construction process, and shorten the training time of the neural network. Finally, the optimized BP neural network is applied to benchmark image denoising and traffic image denoising. The experimental results show that compared with the traditional denoising methods such as Median filtering, Neighborhood average filtering and Wiener filtering, the proposed method has better performance in peak signal-to-noise ratio.

Wind Power Short-Term Forcasting of BP Neural Network Based on the Small-World Optimization

2014 ◽  
Vol 933 ◽  
pp. 384-389
Author(s):  
Xin Zhao ◽  
Shuang Xin Wang
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Bp Neural Network ◽  
Wind Direction ◽  
Wind Farm ◽  
Prediction Method ◽  
Small World ◽  
Power Method ◽  
Short Term

Wind power short-term forcasting of BP neural network based on the small-world optimization is proposed. First, the initial data collected from wind farm are revised, and the unreasonable data are found out and revised. Second, the small-world optimization BP neural network model is proposed, and the model is used on the prediction method of wind speed and wind direction, and the prediction method of power. Finally, by simulation analysis, the NMAE and NRMSE of the power method are smaller than those of the wind speed and wind direction method when the wind power data of one hour later are predicted. When the power method are used to forecast the data one hour later, NMAE is 5.39% and NRMSE is 6.98%.

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