A data prediction method under small sample condition by combining neural network and grey system methods

2010 ◽  
Author(s):  
Jihua Fu ◽  
Jie Tong ◽  
Qian Wang ◽  
Zhongyu Wang
Keyword(s):  
Neural Network ◽  
Prediction Method ◽  
Small Sample ◽  
Grey System ◽  
Data Prediction

scholarly journals A Performance Prediction Method Based on Sliding Window Grey Neural Network for Inertial Platform

2021 ◽  
Vol 13 (23) ◽  
pp. 4864
Author(s):  
Langfu Cui ◽  
Qingzhen Zhang ◽  
Liman Yang ◽  
Chenggang Bai
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Prediction Method ◽  
Sliding Window ◽  
Small Sample ◽  
Grey Theory ◽  
Sensing System ◽  
Performance Change ◽  
Sample Data ◽  
Platform System

An inertial platform is the key component of a remote sensing system. During service, the performance of the inertial platform appears in degradation and accuracy reduction. For better maintenance, the inertial platform system is checked and maintained regularly. The performance change of an inertial platform can be evaluated by detection data. Due to limitations of detection conditions, inertial platform detection data belongs to small sample data. In this paper, in order to predict the performance of an inertial platform, a prediction model for an inertial platform is designed combining a sliding window, grey theory and neural network (SGMNN). The experiments results show that the SGMNN model performs best in predicting the inertial platform drift rate compared with other prediction models.

Energy Consumption Forecasting Using United Grey System–Bayesian Regularization Neural Network Model

2012 ◽  
Vol 524-527 ◽  
pp. 3087-3092 ◽  
Author(s):  
Xiao Hui Hu ◽  
Lv Jun Zhan ◽  
Yun Xue ◽  
Gui Xi Liu ◽  
Zhe Fan
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Regularization Parameter ◽  
Prediction Method ◽  
Neural Model ◽  
Bayesian Regularization ◽  
Grey System ◽  
Bayesian Regularization Neural Network ◽  
Energy Consumption Prediction

The energy consumption of the enterprise is subject to various factors. To solve the problem, a new grey-neural model is proposed which effectively combines the grey system and Bayesian-regularization neural network and avoids the disadvantages of each other. The case study indicates that the prediction method is not only reasonable in theory but also owns good application value in the energy consumption prediction. Meanwhile, results also exhibit that G-BRNN model has the automated regularization parameter selection capability and may ensure the excellent adaptability and robustness.

Vertical Deformation Prediction Method for Bridges Based on Grey System-BP Neural Network Model

2014 ◽  
Vol 578-579 ◽  
pp. 1217-1223
Author(s):  
Ming Jun Li ◽  
Huan Huan Wu ◽  
Xiao Feng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Vertical Deformation ◽  
Grey System ◽  
Single Model ◽  
Deformation Prediction ◽  
Input Sample ◽  
Monitoring And Forecasting ◽  
Bp Neural Network Model

For there are many problems such as large amount interference factors, the amount of data collected is small, and so on occur in monitoring and forecasting, use the model that combine grey system and neural network to forecast. That is predicting with single model first, and then taking the prediction result of grey system as input sample value of BP neural network. Considering the measured values as objective sample value of network, neural network used to deformation forecast can be obtained through training. Prediction results show that this method can obtain a good forecasting result.

Oil Saturation Log Prediction Using Neural Network in New Steamflood Area

2020 ◽  
Author(s):  
A. Syahputra
Keyword(s):  
Neural Network ◽  
Water Saturation ◽  
Prediction Method ◽  
Neural Model ◽  
Training Model ◽  
Acquisition Time ◽  
Oil Saturation ◽  
Full Field ◽  
Remaining Oil ◽  
Observation Wells

Surveillance is very important in managing a steamflood project. On the current surveillance plan, Temperature and steam ID logs are acquired on observation wells at least every year while CO log (oil saturation log or SO log) every 3 years. Based on those surveillance logs, a dynamic full field reservoir model is updated quarterly. Typically, a high depletion rate happens in a new steamflood area as a function of drainage activities and steamflood injection. Due to different acquisition time, there is a possibility of misalignment or information gaps between remaining oil maps (ie: net pay, average oil saturation or hydrocarbon pore thickness map) with steam chest map, for example a case of high remaining oil on high steam saturation interval. The methodology that is used to predict oil saturation log is neural network. In this neural network method, open hole observation wells logs (static reservoir log) such as vshale, porosity, water saturation effective, and pay non pay interval), dynamic reservoir logs as temperature, steam saturation, oil saturation, and acquisition time are used as input. A study case of a new steamflood area with 16 patterns of single reservoir target used 6 active observation wells and 15 complete logs sets (temperature, steam ID, and CO log), 19 incomplete logs sets (only temperature and steam ID) since 2014 to 2019. Those data were divided as follows ~80% of completed log set data for neural network training model and ~20% of completed log set data for testing the model. As the result of neural model testing, R2 is score 0.86 with RMS 5% oil saturation. In this testing step, oil saturation log prediction is compared to actual data. Only minor data that shows different oil saturation value and overall shape of oil saturation logs are match. This neural network model is then used for oil saturation log prediction in 19 incomplete log set. The oil saturation log prediction method can fill the gap of data to better describe the depletion process in a new steamflood area. This method also helps to align steam map and remaining oil to support reservoir management in a steamflood project.

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.

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