scholarly journals Dynamic Curve Fitting and BP Neural Network with Feature Extraction for Mobile Specific Emitter Identification

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Qin Zhang ◽  
Yu Guo ◽  
ZhengYu Song
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Curve Fitting ◽  
Bp Neural Network ◽  
Specific Emitter Identification

Computer-Generated Hologram Compression Research Based on BP Neural Network in Wavelet Domain

2014 ◽  
Vol 971-973 ◽  
pp. 1884-1887 ◽  
Author(s):  
A Lin Hou ◽  
Liang Wu ◽  
Qing Liao ◽  
Chong Jin Wang ◽  
Jun Liang Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Bp Neural Network ◽  
Function Approximation ◽  
Simple Structure ◽  
Inner Product ◽  
Compression Rate ◽  
Wavelet Domain ◽  
Fast Calculation ◽  
Computer Generated Hologram

The algorithm of hologram compression using BP neural network in wavelet domain is proposed. Firstly, computer-generated hologram pretreatment is carried out by wavelet transform. And then the inner product of wavelet and holograms are weighted and used to implement the feature extraction of hologram. Finally, the extracted feature vectors are substituted into neural network so as to implement the function approximation, classification and hologram compression. The experimental results clearly show the feasibility and effectiveness of the method. The compression rate can reach 0.803%and still gets a clear reconstructed image. And the algorithm has the advantages of simple structure and fast calculation speed.

The Character Recognition of Vehicle's License Plate Based on BP Neural Networks

2014 ◽  
Vol 513-517 ◽  
pp. 3805-3808 ◽  
Author(s):  
Wen Bo Liu ◽  
Tao Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Bp Neural Network ◽  
Character Recognition ◽  
License Plate ◽  
Image Preprocessing ◽  
Matrix Operations ◽  
Neural Network Algorithm ◽  
Character Segment

This paper based on license plate image preprocessing ,license plate localization, and character segment ,using BP neural network algorithm to identify the license plate characters. Through k-l algorithm of characters on the feature extraction and recognition of license plate character respectively then taking the extraction of license plate character features into the character classifier to the training. When the end of training, extracting the net-work weights and offset matrix, and storing in the computer. To take the identified character images input to the MATLAB, and with the preservation weights and offset matrix operations, obtain the final results of recognition.

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.

Human–Computer Interactive Gesture Feature Capture and Recognition in Virtual Reality

2020 ◽  
pp. 106480462092413 ◽  
Author(s):  
Fan Zhang
Keyword(s):  
Neural Network ◽  
Virtual Reality ◽  
Feature Extraction ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Extraction Methods ◽  
Scale Invariant ◽  
Feature Vectors ◽  
Accuracy And Precision ◽  
Scale Invariant Feature

With the development of computer technology, the simulation authenticity of virtual reality technology is getting higher and higher, and the accurate recognition of human–computer interaction gestures is also the key technology to enhance the authenticity of virtual reality. This article briefly introduced three different gesture feature extraction methods: scale invariant feature transform, local binary pattern and histogram of oriented gradients (HOG), and back-propagation (BP) neural network for classifying and recognizing different gestures. The gesture feature vectors obtained by three feature extraction methods were used as input data of BP neural network respectively and were simulated in MATLAB software. The results showed that the information of feature gesture diagram extracted by HOG was the closest to the original one; the BP neural network that applied HOG extracted feature vectors converged to stability faster and had the smallest error when it was stable; in the aspect of gesture recognition, the BP neural network that applied HOG extracted feature vector had higher accuracy and precision and lower false alarm rate.

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.

scholarly journals Research on Crop Leaf Disease Identification Method Based on LM-BP Neural Network

2019 ◽  
Vol 131 ◽  
pp. 01118
Author(s):  
Fan Tongke
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Bp Neural Network ◽  
Extraction Method ◽  
Large Scale ◽  
Disease Diagnosis ◽  
Learning Technology ◽  
Feature Extraction Method ◽  
Disease Identification

Aiming at the problem of disease diagnosis of large-scale crops, this paper combines machine vision and deep learning technology to propose an algorithm for constructing disease recognition by LM_BP neural network. The images of multiple crop leaves are collected, and the collected pictures are cut by image cutting technology, and the data are obtained by the color distance feature extraction method. The data are input into the disease recognition model, the feature weights are set, and the model is repeatedly trained to obtain accurate results. In this model, the research on corn disease shows that the model is simple and easy to implement, and the data are highly reliable.

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.

Feature Extraction and Recognition Based on the Biological Analysis

2014 ◽  
Vol 513-517 ◽  
pp. 3180-3183
Author(s):  
Wen Cang Zhao ◽  
Fan Wang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Bp Neural Network ◽  
Characteristic Parameters ◽  
Invariant Moments ◽  
Biological Analysis ◽  
Stored Product Pests ◽  
Abdominal Perimeter ◽  
Stable Characteristic

In this paper the extracted features including rectangularity,elongation, invariant moments and the four ratios of the stored product pests, which are the ratio of antennae area to torso area, the ratio of antennae perimeter to torso perimeter,the ratio of head and chest area to abdominal area, the ratio of head and chest perimeter to abdominal perimeter. Then these 13 characteristic parameters are input to BP neural network and SVM for recognition and classification. Form the results we can see that the 13 features in this paper can be well reflected the stable characteristic information of the stored product pests.

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