Study on Application of Quantum BP Neural Network to Curve Fitting

2013 ◽  
Vol 11 (10) ◽  
Author(s):  
Zhixia Zhang ◽  
Yanchang Liu ◽  
Jianping Xie
Keyword(s):  
Neural Network ◽  
Curve Fitting ◽  
Bp Neural Network

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.

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.

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.

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 Electric Energy Substitution Potential Prediction Based on Logistic Curve Fitting and Improved BP Neural Network Algorithm

2019 ◽  
Vol 25 (3) ◽  
pp. 18-24 ◽  
Author(s):  
Yujun Liu ◽  
Cheng Hu ◽  
Yi Hong
Keyword(s):  
Neural Network ◽  
Influencing Factors ◽  
Curve Fitting ◽  
Bp Neural Network ◽  
Electric Energy ◽  
Logistic Curve ◽  
Energy Substitution ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
The Impact

In order to predict the potential of electric energy substitution in the next decade in China, this paper proposes a prediction method based on Logistic curve fitting and improved BP neural network algorithm. The amount of electric energy substitution is defined to quantify the potential of electric energy substitution. Then the important influencing factors of electric energy substitution based on the Impact by Population, Affluence and Technology (IPAT) model are established and quantified. For different influencing factors, logistic curve fitting and polynomial function fitting method were used to estimate the data fitting. A two-node output layer model of BP neural network is established and improved with additional momentum factors and adaptive learning rate to learn and train the data related to electric energy substitution from 2003 to 2017, and calculate the amount of electric energy substitution which are substitution potential from 2018 to 2020, 2025 and 2030. The calculation results show that the method has higher computational accuracy and fewer iterations. The prediction results are reasonable and effective, which can be the reference of the research of energy substitution.

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