Temperature compensation of high-precision I/F converter based on BP neural network

2011 ◽  
Author(s):  
Mingjie Dong ◽  
Bo Wang ◽  
Yongsheng Shi
Keyword(s):  
Neural Network ◽  
High Precision ◽  
Bp Neural Network ◽  
Temperature Compensation

scholarly journals The Research of Temperature Compensation for Thermopile Sensor Based on Improved PSO-BP Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Yuanjiang Li ◽  
Yuehua Li ◽  
Feng Li ◽  
Bin Zhao ◽  
QingQing Li
Keyword(s):  
Neural Network ◽  
Thermal Radiation ◽  
Bp Neural Network ◽  
Temperature Compensation ◽  
Safety Monitoring ◽  
Bp Algorithm ◽  
Bp Network ◽  
The Core ◽  
Thermopile Sensor ◽  
Improved Pso

When thermopile sensor is used for safety monitoring of equipment in industrial environments, particularly for measuring the thermal radiation information of device, the measured result of this kind of sensor is usually affected by ambient temperature due to its unique structure. An improved PSO-BP algorithm is proposed for temperature compensation of thermopile sensor and correcting the error in the condition of the system accuracy requirements reduced by temperature. The core of improved PSO-BP algorithm is to improve the certainty of initial weights and thresholds that belonged to BP neural network and then train the samples by using BP neural network for enhancing the generalization ability and stability of system. The experimental results show that the proposed PSO-BP network outperforms other similar algorithms with faster convergence speed, lower errors, and higher accuracy.

scholarly journals Technical and Tactical Diagnosis Model of Table Tennis Matches Based on BP Neural Network

2021 ◽  
Author(s):  
Wenwen Huang ◽  
Miaomiao Lu ◽  
Yuxuan Zeng ◽  
Mengyue Hu ◽  
Yi Xiao
Keyword(s):  
Neural Network ◽  
High Precision ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Diagnostic Model ◽  
Table Tennis ◽  
Winning Probability ◽  
Highly Efficient ◽  
Diagnosis Model

Abstract Background: The technical and tactical diagnosis of table tennis is extremely important for the preparation of matches, and there is a nonlinear relationship between athletes’ performance and their sports quality. As the neural network model has high nonlinear dynamic processing ability and has high fitting accuracy, the main purpose of this study was to establish a technical and tactical diagnosis model of table tennis matches based on a neural network to diagnose the influence of athletes’ techniques and tactics on the competition result. Methods: A three-layer back propagation neural network model for table tennis match diagnosis were established. The 30 technical and tactical analysis indexes that are closely related to winning a competition were selected based on the double three-phase evaluation method. And 100 table tennis matches were selected as data sample, of which 70 matches were taken as training sample to establish the diagnostic model, the other 30 matches were used to test the validity of the diagnostic model.Results: The technical and tactical diagnosis model of table tennis matches based on BP neural network had a high precision up to 99.997% and highly efficient in fitting (R2 = 0.99). It had a good ability to diagnose the technical and tactical abilities of table tennis players. The technical and tactical diagnosis results showed that the scoring rate of the fourth stroke of Harimoto had the greatest influence on the winning probability.Conclusion: The technical and tactical diagnosis model of table tennis matches based on BP neural network had a high precision and highly efficient in fitting. By using this model, the weights of the influence of athletes’ technical and tactical indexes on the winning probability of the competition can be calculated, which provides a valuable reference for formulating targeted training plans for players.

scholarly journals Study on selecting of prefabricated plant site basing on BP neural network

2018 ◽  
Vol 53 ◽  
pp. 03073
Author(s):  
Yao Gang ◽  
Yang Yang ◽  
Shen Xin ◽  
Li Jun
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
High Precision ◽  
Bp Neural Network ◽  
Raw Materials ◽  
Output Factor ◽  
Plant Site ◽  
Selection Of ◽  
Transportation Facilities

In this paper, the evaluation and prediction model of prefabricated plant site was established by BP neural network, which taking nine factors into consideration, such as location, topography, land scale, transportation facilities, availability of raw materials and labour. These nine factors were taken as input factors, and the normalized global value was taken as output factor. The normalized global value was used to evaluate the performance of prefabricated plant site. In addition, the model was verified to be accurate by analysing twelve prefabricated plant site samples. Therefore, it is obvious that the model is stable in operation with high precision, and can provide effective support in the selection of prefabricated plant site.

Study on Optimization of Machining Parameters in Electrochemical Machining

2012 ◽  
Vol 476-478 ◽  
pp. 375-378
Author(s):  
Lei Wang ◽  
Jiang Ning Gai
Keyword(s):  
Neural Network ◽  
High Precision ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Three Dimensional ◽  
Electrochemical Machining ◽  
Machining Accuracy ◽  
Machining Parameters ◽  
Bp Neural Network Model ◽  
Complicated Surface

In electrochemical machining (ECM) machining accuracy of workpieces is greatly influenced by many machining parameters. In this paper the BP neural network model is employed to optimize machining parameters in ECM. Taking a three dimensional complicated surface as the research object the experimental results have shown that the optimization process can be easily operated and the result is of high precision.

Prediction for Percentage of Vehicle Entering Expressway Rest Area Based on BP Neural Network

2012 ◽  
Vol 178-181 ◽  
pp. 1956-1960
Author(s):  
Xiao Yan Shen ◽  
Hao Xue Liu ◽  
Jia Liu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Influencing Factors ◽  
High Precision ◽  
Bp Neural Network ◽  
High Speed ◽  
High Speed Railway ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Influence Degree

In order to scientifically decide the percentage of vehicle entering expressway rest area, based on analyzing the influencing factors relating to the percent of mainline traffic stopping, a BP neural network prediction model for it was put forward. Finally, The Xinzheng Rest Area (XRA) was taken as an example for verifying the feasibility of the prediction model and determining the influence degree of the Shijiazhuang-Wuhan high-speed railway on the percentage of mainline vehicles entering XRA. The result shows that the model had a high precision and reliability.

Technology of NC Machine Error Parameter Identification Based on BP Neural Network

2013 ◽  
Vol 423-426 ◽  
pp. 2399-2403
Author(s):  
Yao Ming Li ◽  
Xing Quan Shen
Keyword(s):  
Neural Network ◽  
Measurement Error ◽  
Parameter Identification ◽  
High Precision ◽  
Bp Neural Network ◽  
Three Dimensional ◽  
Probe Measurement ◽  
Machine Error ◽  
Nc Machine ◽  
Error Parameter

NC machines touch-trigger probes generally have three-dimensional measuring function, its working principle is equivalent to a duplication of contacts with high precision switch, under the control of the measurement software, to achieve high-precision machining center automatically detected. Paper proposed based on BP neural network in NC machines tool probe measurement error parameter identification method. Through theoretical analysis and experimental research to determine the probe installation error parameters and the probe measurement error parameters of the model, but also identify ways to eliminate probe system error term uncertainty of the condition, in order to determine the detection method and planning of testing provides a reliable path basis.

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