Research on Correction Method of Sound Channel Velocity of Ultrasound Flowmeter Based on BP Neural Network

2019 ◽  
Vol 07 (03) ◽  
pp. 186-192
Author(s):  
晶晶 李
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Correction Method ◽  
Sound Channel ◽  
Velocity Of Ultrasound ◽  
Channel Velocity

Diagnosis for Engine Misfire Fault Based on Torsional Vibration and Neural-Network Analysis

2012 ◽  
Vol 433-440 ◽  
pp. 7240-7246
Author(s):  
Can Yi Du ◽  
Kang Ding ◽  
Zhi Jian Yang ◽  
Cui Li Yang
Keyword(s):  
Neural Network ◽  
Torsional Vibration ◽  
Bp Neural Network ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Correction Method ◽  
Operating Conditions ◽  
Diagnostic Model ◽  
Vibration Signal Analysis

Misfire is a common fault which affects the engine performances. Because the signal-to-noise ratio of torsional vibration signal is high, torsional vibration test and analysis for the engine were performed in a variety of operating conditions, including healthy condition and single-cylinder misfire condition. In order to improve the accuracy of analysis, energy centrobaric correction method was used to correct the amplitude. Taking the corrected amplitude of main order as the fault feature, and then a BP neural-network diagnostic model can be established for misfire diagnosis. The result shows that the method of combining torsional vibration signal analysis and neural-network can diagnose engine misfire fault correctly.

Application of BP Neural Network in PSD Nonlinear Correction

2014 ◽  
Vol 651-653 ◽  
pp. 400-404 ◽  
Author(s):  
Zhuo Jing Yang ◽  
Jian Wei Zhang ◽  
Wen Jie Hao ◽  
Jin Ping Yang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Nonlinear Problems ◽  
Correction Method ◽  
Small Displacement ◽  
Nonlinear Correction ◽  
Remarkable Effect ◽  
System Requirements ◽  
Position Sensitive ◽  
Arbitrary Precision

Because resistance of two-dimensional position sensitive detector's (PSD) photo surface is not absolute uniformity that its output is nonlinear. It is this feature enables the PSD difficult to measure small displacement. In order to solve this problem, BP neural network is proposed to solve the problem of PSD nonlinear correction after the study of traditional nonlinear correction method; BP neural network would have a strong ability of nonlinear mapping after training, and it can approach arbitrarily contact function by arbitrary precision, and MATLAB neural networking boxes can simulate BP neural network easily. Simulation and verification indicate that the method has a remarkable effect in solving nonlinear problems, and it can meet system requirements.

scholarly journals Error Correction Method of Crude Oil Moisture Content Detection Based on BP Neural Network

2020 ◽  
Vol 558 ◽  
pp. 022005
Author(s):  
Pengmin Dong ◽  
Xianghu Zeng ◽  
Chengcai Duan ◽  
Tianqi Wang ◽  
Shichong Luo ◽  
...  
Keyword(s):  
Neural Network ◽  
Moisture Content ◽  
Crude Oil ◽  
Error Correction ◽  
Bp Neural Network ◽  
Correction Method ◽  
Error Correction Method

scholarly journals Study of BP neural network based on improved particle swarm algorithm

2021 ◽  
Vol 2083 (3) ◽  
pp. 032041
Author(s):  
Xiaoqian Ma ◽  
Liyuan Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Time Series Data ◽  
Arima Model ◽  
Particle Swarm ◽  
Correction Method ◽  
Series Data ◽  
Particle Swarm Algorithm ◽  
The Neural Network ◽  
Swarm Algorithm

Abstract This paper uses first-order difference to transform non-smooth data into smooth time series data, determines the p and q parameters in the model by judging the trailing and truncated nature of ACF, PACF, and finally establishes the ARIMA model after ACI, BCI detection. According to the parameters of the neural network randomly selected similar to the initial spatial position of the particles in the particle swarm algorithm, the improved particle swarm algorithm is used instead of the gradient correction method to precisely adjust the parameters and establish the BP neural network, which improves the robustness and accuracy of the prediction model.

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