Study of Fitting Methods of Giant Magneto-Impedance Sensors’ Output Signals

2014 ◽  
Vol 556-562 ◽  
pp. 4880-4883
Author(s):  
Jing Xiao ◽  
Xiu Sheng Duan ◽  
Zhe Feng
Keyword(s):  
Neural Network ◽  
Magnetic Field ◽  
Curve Fitting ◽  
Bp Neural Network ◽  
Measurement Precision ◽  
Complex Environment ◽  
Gmi Effect ◽  
Effect Curve ◽  
Giant Magneto Impedance ◽  
Sine Functions

As magnetic field varies greatly under complex environment, it is difficult for GMI sensor to adapt. On basis of analyzing the curve of GMI effect, curve fitting methods with polynomial, sum of sine functions and the BP neural network are studied to find the best one to fit the sensors’ outputs. When the best one is acted as model of the GMI sensor for measurement, the measurement precision can be effectively improved and the sensors' range can be greatly expanded.

Research of Oilfield Production Forecast Based on Least Squares Fitting and Improved BP Neural Network

2013 ◽  
Vol 333-335 ◽  
pp. 1456-1460 ◽  
Author(s):  
Wen Bo Na ◽  
Zhi Wei Su ◽  
Ping Zhang
Keyword(s):  
Neural Network ◽  
Least Squares ◽  
Curve Fitting ◽  
Bp Neural Network ◽  
Least Squares Method ◽  
Forecast Error ◽  
Production Forecast ◽  
Least Squares Fitting ◽  
Weak Points ◽  
Lm Algorithm

A new method which is least squares fitting combined with improved BP neural network based on LM algorithm was put forward. In order to overcome the weak points that easy to fall into local minimum, slow convergence of traditional BP neural network, we use LM algorithm to improve it. Least-squares curve fitting can be used to reflect the overall trend of the data changes, so we adopted least squares method firstly to make curve fitting for sample data firstly. Then, we corrected the fitting error by the improved BP Neural Network which has the advantages that reflecting external factors. Finally, the fitted values and error correction values were added to get oilfield production forecast. The results show that the oilfield production forecast error is significantly lower than the single curve fitting, BP Neural Network or LMBP.

Research on Cam Curve-Fitting Based on ANN

2013 ◽  
Vol 321-324 ◽  
pp. 2157-2160
Author(s):  
Wan Qiang Hu
Keyword(s):  
Neural Network ◽  
Curve Fitting ◽  
Bp Neural Network ◽  
Fast Computation

The theory and algorithm of BP neural network were introduced, and it was trained by the theoretical amounts of corresponding angle of Cam, then the curve-fitting was obtained. The result proved that the fast computation of the theoretical amounts of any angle in Cam carve-fitting could be achieved by means of BP neural network.

BP Neural Network Fitting for Spectra of Blast Furnace Raceway

2011 ◽  
Vol 55-57 ◽  
pp. 197-202
Author(s):  
Bo Yang ◽  
Ning Li ◽  
Liang Lei ◽  
Xue Wang
Keyword(s):  
Neural Network ◽  
Blast Furnace ◽  
Curve Fitting ◽  
Bp Neural Network ◽  
Radiation Spectrum ◽  
Back Propagation ◽  
Early Stopping ◽  
Spectral Fitting ◽  
Levenberg Marquardt ◽  
Effective Radiation

Three-layer BP neural network, particularly using Levenberg-Marquardt back-propagation with early stopping algorithm, is widely used in curve fitting, attributing to its fast speed and free from over-fitting. Hence, the trained network by Levenberg-Marquardt back-propagation was used for curve fitting of the radiation spectrum of blast furnace raceway. The results showed that Levenberg-Marquardt back-propagation with early stopping algorithm presented a better fitting ability. Additionally, the results of spectral fitting model showed that the blast furnace raceway had an effective radiation spectrum in the wavelength range from 420nm to 880nm, where the raceway could be considered as the gray body radiation.

Investigation of magnetic field anneal in micro-patterned amorphous ribbon on giant magneto-impedance effect enhancement

Sensor Review ◽  
2019 ◽  
Vol 39 (3) ◽  
pp. 309-317 ◽  
Author(s):  
Zhu Feng ◽  
Shaotao Zhi ◽  
Lei Guo ◽  
Chong Lei ◽  
Yong Zhou
Keyword(s):  
Magnetic Field ◽  
Line Width ◽  
Longitudinal Magnetic Field ◽  
Amorphous Ribbon ◽  
Domain Wall Motion ◽  
Longitudinal Field ◽  
Content Type ◽  
Demagnetizing Field ◽  
Gmi Effect ◽  
Giant Magneto Impedance

Purpose This paper aims to investigate magnetic field anneal in micro-patterned Co-based amorphous ribbon on giant magneto-impedance (GMI) effect enhancement. Design/methodology/approach The amorphous ribbons were annealed in transverse and longitudinal magnetic field. The influence of different field annealing directions on GMI effect and impedance Z, resistance R and reactance X with a series of line width have been deeply analyzed. Findings In comparison with GMI sensors microfabricated by unannealed and transversal field annealed ribbons, GMI sensor which was designed and microfabricated by longitudinal field anneal ribbon performs better. The results can be explained by the domain wall motion and domain rotation during annealing process and the geometric structure of Co-based GMI sensor. In addition, shrinking the line width of GMI sensor can promote GMI effect significantly because of the effect of demagnetizing field, and the optimum GMI ratio is 209.7 per cent in longitudinal field annealed GMI sensor with 200 μm line width. Originality/value In conclusion, annealing in longitudinal magnetic field and decreasing line width can enhance GMI effect in micro-patterned Co-based amorphous ribbon.

Analysis of Hydrological Data Based on BP Neural Network Approximation and Polynomial Fitting

2012 ◽  
Vol 518-523 ◽  
pp. 4115-4118
Author(s):  
Shou Jun Li ◽  
Xiao Ping Ma ◽  
Hong Yu
Keyword(s):  
Neural Network ◽  
Error Analysis ◽  
Curve Fitting ◽  
Bp Neural Network ◽  
Polynomial Fitting ◽  
Bp Network ◽  
Hydrological Data ◽  
Input Sample ◽  
Important Means ◽  
Neuronal Number

It is an important means of hydrological data analysis for drawing hydrological data curve. The paper conducts a study on drawing method of stage-discharge curve in two aspects including BP neural network approximation and curve fitting, according to data extracted from a hydrologic station located in Suqian section of Beijing-Hangzhou Canal. Normalization of the input sample is processed in order to caculate conveniently and prevent partial neurons to supersaturate. Then, neuronal number is determined by method of heuristics. And the transfer function and training function are finalized on the premise of target error 0.0001.Error analysis is performed after simulation of BP network approximation. 2- and 3-order curve fitting is done based on principle of least squares of polynomial fitting, then followed by error analysis. Comparison of both methods comes to the conclusion that approximation of BP network for a given data is more accurate than that of curve fitting.

scholarly journals The Design of the Biomagnetic Field Sensor without Magnetic Shielding

2019 ◽  
Vol 16 (04) ◽  
pp. 1950019
Author(s):  
Ming Xu ◽  
Changlin Han ◽  
Hui Min Lu ◽  
Junhao Xiao ◽  
Jingsheng Tang ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Wearable Sensors ◽  
High Sensitivity ◽  
Magnetic Shielding ◽  
Small Range ◽  
Natural Conditions ◽  
Gmi Effect ◽  
Weak Intensity ◽  
Field Sensor ◽  
Giant Magneto Impedance

Due to the extremely weak intensity of the biomagnetic field and the serious interference from the environmental magnetic field, the detection of the biomagnetic field becomes such challenging work. After analyzing the deficiencies in the current biomagnetic field sensors, this paper proposes and realizes a high-sensitivity magnetic field sensor, based on the giant magneto-impedance (GMI) effect. Taking advantage of the miniaturized magnetic probe, the multistage multiple amplification and the multiband interference suppression, our sensor mainly makes three achievements: the pT level magnetic resolution, the ability to detect the muscle magnetic field without the magnetic shielding and the resistibility to a small-range wobbling in the state of working, which makes it possible to detect the biomagnetic field by wearable sensors under natural conditions.

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