Experimental Study on Calibration Model Based on Pt100 Temperature Sensor

2013 ◽  
Vol 798-799 ◽  
pp. 402-406
Author(s):  
Peng Fei Li ◽  
Cheng Yv ◽  
Yong Ping Yang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Bp Neural Network ◽  
Temperature Sensor ◽  
Least Square Method ◽  
Least Square ◽  
Calibration Model ◽  
Calibration Experiment ◽  
The Relationship ◽  
Lm Algorithm

In order to improve measuring-temperature accuracy of the PT100 temperature sensor, we conduct multi-point calibration experiment. The BP neural network based on LM algorithm can process experimental data and the least square method can fit out more accurate formula that express the relationship between the temperature and resistance. It is available that this arithmetic that the interrelated experiment demonstrate its accuracy improve precision of the PT100 temperature sensor.This arithmetic can be applied to the calibration test.

Modeling of Pulp Washing Process Based on Two-Step BP Neural Network

2012 ◽  
Vol 198-199 ◽  
pp. 1712-1715
Author(s):  
Hua Zhong Wang ◽  
Wen Juan Shan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Model Simulation ◽  
Least Square Method ◽  
Least Square ◽  
Washing Process ◽  
Optimization Control ◽  
Important Quality ◽  
Simulation Results ◽  
Quality Indexes

The most important quality indexes to evaluate pulp washing performance are residual soda and the Baume degree. But it is difficult to measure the two indexes directly. To solve the problem of optimization control of the washing process, the model of the residual soda and the Baume degree are studied in this paper. Simulating residual soda and the Baume degree via a two-step neural network and modeling them based on least square method and steady-state data obtained by neural network model. Simulation results show that this method can effectively locate the pulp washing process.

Research on the Influence of PEG to the Nuclei Population Density of Copper Electrocrystallization

2013 ◽  
Vol 652-654 ◽  
pp. 787-790
Author(s):  
Qing Mei Wang ◽  
Feng Yan Sun
Keyword(s):  
Experimental Data ◽  
Population Density ◽  
Least Square Method ◽  
Least Square ◽  
Number Density ◽  
Glass Carbon Electrode ◽  
Sulphate Electrolyte ◽  
Glass Carbon ◽  
Relationship Of ◽  
The Relationship

Influences of the concentration of additive PEG and overpotential on the Nuclei population density of copper electrocrystallization on a glass carbon electrode (GCE) have been studied in this file. We characterized the experimental data extracted from Li’s work [10] with the least square method and exponential curves. The relationship of nucleation number density and overpotential follows the exponential function basically has been obtained by carrying on the data fitting to experimental data. And with a given overpotential, when inject PEG into acidic cupric sulphate electrolyte with a lower concentration, nucleation number density is reduced, but when the injection concentration of PEG is high enough, nucleation number density is increased instead. While with a certain concentration of PEG, as the overpotential more negative, the nuclei population density increased gradually, but if the injection concentration is low, the change of the growth rate of nucleation number density is not significant.

scholarly journals A New Approach of Disaster Forecasting Based on Least Square Optimized Neural Network

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Fanbao Meng ◽  
Suolin Jing ◽  
Xizhen Sun ◽  
Changxiang Wang ◽  
Yanbo Liang ◽  
...  
Keyword(s):  
Neural Network ◽  
Rock Burst ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Least Square Method ◽  
Mining Area ◽  
Least Square ◽  
Control Factors ◽  
General Law ◽  
The Impact

The evaluation of the risk is the prerequisite for the implementation of countermeasures in the prevention and control of rock burst, and the research on the fast forecast at scene of the rock burst is more important for the safety production of coal mine. Aiming at the problem that dynamic disasters caused by many factors and heterogeneity of coal and rock are difficult to predict in the process of coal mining, in this paper, the general law and the risk control factors of the rock burst are studied, a mathematical model based on the BP neural network was built according to the different actual mining conditions in the mining area, and the output layer has obtained the prediction result. Then, the results of the output samples after training were fitted by using SPSS software, and the fitting function was obtained by multiple least square fitting. Finally, the fitting results are checked by the data of actual coal mine dynamic disaster parameters. The prediction results show that the simulation results of BP neural network prediction model and the fitting function of the least square method can reduce the impact of subjective judgment on the prediction results, and the application of the fitting function can obtain the prediction results in the first time to ensure the construction safety. The method of on-site hazard assessment and inspection by using fitting function is simple and feasible and has high accuracy, which provides a new idea for the field prediction of rock burst.

Research on Treatment of Retaining Wall Foundation with Geosynthetics Based on BP Neural Network

2020 ◽  
Vol 852 ◽  
pp. 220-229
Author(s):  
Rui Li
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Least Squares ◽  
Adaptive Learning ◽  
Bp Neural Network ◽  
Retaining Wall ◽  
Maximum Error ◽  
Least Square ◽  
Wall Foundation ◽  
Bp Neural Network Model

Through the long-term load creep test of CE131 geonet and SD L25 retaining wall foundation, which are widely used in reinforced earth engineering, a large number of experimental data are obtained. On this basis, the least-squares and BP neural network are used to predict its creep variables. The principle of least squares is to find a curve in the curve family to fit the experimental data. From the sum of the squared errors σ = 0. 001 16, the fitting accuracy is higher. The BP neural network has adaptive learning and memory capabilities, especially the three-layer BP neural network model. The maximum error between the predicted value and the actual value is 0.91%, which is a lot better than the error of the least square 3.4%. This method Found a new way for creep prediction.

Research on Characteristics of Horizontal Atmospheric Diffusion Coefficient Based on BP Neural Network

2013 ◽  
Vol 791-793 ◽  
pp. 1605-1608 ◽  
Author(s):  
Pin Shang ◽  
Cheng Dong Wu ◽  
Ren Ke Han ◽  
Wen Jia Ma
Keyword(s):  
Neural Network ◽  
Diffusion Coefficient ◽  
Bp Neural Network ◽  
Characteristic Curve ◽  
Least Square Method ◽  
Measured Data ◽  
Least Square ◽  
Atmospheric Diffusion ◽  
Actual Application ◽  
Trained Neural Network

In order to obtain the actual characteristics of horizontal atmospheric diffusion direction, based on Gauss plume model and the measured data, we use BP neural network to fit the characteristic curve of the diffusion coefficient. We establish a BP neural network, and then we train the network and simulate the diffusion coefficient. According to the simulation results, we compare the characteristic curve with the curve based on the least square method. And the results show that the characteristic curve based on BP neural network has better fitting accuracy. Hence, using the trained neural network to predict the diffusion coefficient has certain theory meaning and actual application value.

scholarly journals A Method of Ore Blending Based on the Quality of Beneficiation and Its Application in a Concentrator

2021 ◽  
Vol 11 (11) ◽  
pp. 5092
Author(s):  
Bingyu Liu ◽  
Dingsen Zhang ◽  
Xianwen Gao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Industrial Test ◽  
Modeling Method ◽  
Daily Work ◽  
Production Site ◽  
Neural Network Algorithm ◽  
The Relationship ◽  
Ore Dressing

Ore blending is an essential part of daily work in the concentrator. Qualified ore dressing products can make the ore dressing more smoothly. The existing ore blending modeling usually only considers the quality of ore blending products and ignores the effect of ore blending on ore dressing. This research proposes an ore blending modeling method based on the quality of the beneficiation concentrate. The relationship between the properties of ore blending products and the total concentrate recovery is fitted by the ABC-BP neural network algorithm, taken as the optimization goal to guarantee the quality of ore dressing products at the source. The ore blending system was developed and operated stably on the production site. The industrial test and actual production results have proved the effectiveness and reliability of this method.

Approximation of the Kinematics of Foot Joints by Using Passive Elastic Characteristics of Joint

2007 ◽  
Vol 342-343 ◽  
pp. 621-624
Author(s):  
Hyeon Ki Choi ◽  
Si Yeol Kim ◽  
Won Hak Cho
Keyword(s):  
Joint Angle ◽  
Reaction Force ◽  
Force Plate ◽  
Least Square Method ◽  
Least Square ◽  
Biomechanical Analysis ◽  
Clinical Evaluations ◽  
Foot Joints ◽  
The Relationship ◽  
Elastic Characteristics

We investigated the relationship between kinematic and kinetic characteristics of foot joints resisting ground reaction force (GRF). Passive elastic characteristics of joint were obtained from the experiment using three cameras and one force plate. The relationship between joint angle and moment was mathematically modeled by using least square method. The calculated ranges of motion were 7o for TM joint, 4o for TT joint and 20o for MP joint. With the model that relates joint angle and plantar pressure, we could get the kinematic data of the joints which are not available from conventional motion analysis. The model can be used not only for biomechanical analysis which simulates gait but also for the clinical evaluations.

Parameters Identification for Nonlinear Duffing System Based on Wavelet Transformation

2013 ◽  
Vol 357-360 ◽  
pp. 1524-1530
Author(s):  
Shi Zhou ◽  
Dong Mei Huang ◽  
Wei Xin Ren ◽  
Qiong Li Wang
Keyword(s):  
Forced Vibration ◽  
Wavelet Transformation ◽  
Recognition Accuracy ◽  
Vibration Signal ◽  
Least Square Method ◽  
Least Square ◽  
Instantaneous Amplitude ◽  
Approximate Analytical Expression ◽  
Duffing System ◽  
The Relationship

Continuous wavelet transformation is made to identify the parameters of damped harmonic forced vibration Duffing system. With the aid of conversion relationship between the scale and frequency, the solution of nonlinear Duffing equation is adopted by average method, which gained approximate analytical expression for instantaneous amplitude and instantaneous frequency of the system. The nonlinear stiffness coefficient and natural frequency can be gained by least square method and the relationship between recognition accuracy and parameter selection are summarized in the article. Parameter identification method of harmonic forced vibration system is proposed in this paper. Studying the wavelet ridge and corresponding scale by segments to filter out the affects of the simple harmonic motion, to extract systems free vibration signal and to achieve the goal of identifying system parameters.

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