Automatic Recognition Analysis of Fabric Structure Based on GLCM and BP Neural Network

2011 ◽  
Vol 332-334 ◽  
pp. 1167-1170
Author(s):  
Chang Sheng Zhang ◽  
Wei Ke ◽  
Guo He Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Light Condition ◽  
Visual Measurement ◽  
Fabric Structure ◽  
Great Possibility ◽  
Occurrence Matrix ◽  
Fabric Structures ◽  
Very High

At present, the work to analyze fabric structure still depends on artificial visual measurement, which is easily influenced by personal sight, mood, mental state as well as light condition. With the development of image processing technology and artificial intelligence, automatic analysis on fabric structure as a replacement of manual labor is of great possibility. In this study, features of fabric-image have been extracted by GLCM (Gray Level Co-occurrence Matrix). These features were analyzed by employing a three layer BP neural network. Three kinds of fabric structures such as plain, twill and satin was verified and the accurate recognition rate is very high to 93.45%.

Machine-Printed Invoice Number Based on Fuzzy Recognition

2012 ◽  
Vol 214 ◽  
pp. 705-710 ◽  
Author(s):  
Xiao Ping Xian
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Real System ◽  
Recognition Method ◽  
Recognition Time ◽  
Recognition Ability ◽  
System Requirements ◽  
Fuzzy Recognition ◽  
Detection And Separation

A new fuzzy recognition method of machine-printed invoice number based on neural network is presented. This method includes ten links: invoice number detection and separation of right on top of invoice, binarization, denoising, incline correction, extraction of invoice code numerals, window scaling, location standardization, thinning, extraction of numeral feature and fuzzy recognition based on BP neural network. Through testing, the recognition rate of this method can be over 99%.The recognition time of characters for character is less than 1 second, which means that the method is of more effective recognition ability and can better satisfy the real system requirements.

The Recognition of Handwritten Digital Based on BP Neural Network

2013 ◽  
Vol 416-417 ◽  
pp. 1239-1243
Author(s):  
Shan Gao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Practical Significance ◽  
Test Results ◽  
Recognition Method ◽  
Number Recognition ◽  
Recognition Ability ◽  
Fuzzy Recognition ◽  
Zip Code

The article put forward to new recognition method of handwritten digital based on BP neural network. Its recognition process mainly includes ten aspect: incline correction of handwritten number, edge detection and separation of a set number, binarization, denoising, extraction of numerals, window scaling, location standardization, thinning, extraction of numeral feature and fuzzy recognition based on BP neural network. The test results show that the recognition rate of this method can be over 92 percent. The recognition time of characters for character is less than 1.1 second, which means that the method is more effective recognition ability and can better satisfy the real system requirements.It should be widely applied practical significance for Book Number Recognition, zip code recognition sorting.

Fault Detection of Carbide Anvil Based on Hurst Exponent and BP Neural Network

2013 ◽  
Vol 805-806 ◽  
pp. 1881-1886 ◽  
Author(s):  
Li Han ◽  
Bin Chen ◽  
Bao Cheng Gao ◽  
Zhao Li Yan ◽  
Xiao Bin Cheng
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Bp Neural Network ◽  
Hurst Exponent ◽  
Recognition Rate ◽  
Pulse Signal ◽  
Characteristic Parameters ◽  
Threshold Method ◽  
Fault Recognition ◽  
Bp Neural Network Model

This paper proposed a novel diagnosis algorithm based on Hurst exponent and BP neural network to detect carbide anvil fault in synthetic diamond industry. Firstly, a sort of preprocessing algorithm is proposed, which uses the sliding window and energy threshold method to separate the pulse from initial continuous signal. Then, some characteristic parameters which are based on Hurst exponent are extracted from the separated pulse signal. These characteristic parameters are used to construct fault characteristic vectors. Finally, the BP neural network model was established for fault recognition. Experimental results show that the proposed fault detection method has high recognition rate of 96.7%.

An Optimized BP Neural Network Based on DS Evidential Reasoning on Heart Disease Prediction

2012 ◽  
Vol 263-266 ◽  
pp. 3342-3347
Author(s):  
Nan Nan Xie ◽  
Fei Yan Chen ◽  
Kuo Zhao ◽  
Liang Hu
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Evidential Reasoning ◽  
Self Organization ◽  
Uncertain Information ◽  
Data Set ◽  
Time Prediction ◽  
Minimum Points

BP neural network is a widely used neural network, with advantages as adaptability, fault tolerance and self-organization. However, BP neural network is difficult to determine the network structure, and easy to fall into local minimum points. In this paper, an optimized BP neural network was proposed based on DS, he advantages of DS Evidential Reasoning on uncertain information are used to improve the recognition rate and credibility of BP. Experiments on Heart Disease Data set shows the proposed method have good performance on run time, prediction accuracy and robustness.

Application BP Neural Network in the Speaker Recognition Based on Chaos Particle Swarm Optimization Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 2805-2808
Author(s):  
Guo Wen Wang ◽  
Shi Xin Luo ◽  
Li He ◽  
Gang Yin
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Speaker Recognition ◽  
Particle Swarm Optimization Algorithm ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Bp Network ◽  
Swarm Optimization

According to the question that BP Neural Network has slow velocity of convergence and is apt to fall into the minimum value, chaos thought is adopted in the particle swarm optimization (PSO). For this, chaos particle swarm optimization algorithm, which improve the ability of getting rid of fractional extreme point in the PSO, is presented and applied to the BP network exercise so that the calculation accuracy and velocity of convergence of BP network are increased. The method of training the BP network for speaker recognition, the recognition rate and speed of training have been greatly improved, making the speaker recognition based on BP neural network to get better results.

Color Face Recognition Based on Quaternion Zernike Moment Invariants and Quaternion BP Neural Network

2013 ◽  
Vol 446-447 ◽  
pp. 1034-1039 ◽  
Author(s):  
Bei Jing Chen ◽  
Hua Zhong Shu ◽  
Gang Chen ◽  
Jun Ge
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Zernike Moment ◽  
Moment Invariants ◽  
Intensity Information ◽  
Active Research ◽  
Facial Images ◽  
Color Face Recognition

As an active research topic, many algorithms have been presented for face recognition. However, they mainly utilize the monochromatic intensity information. Among a few color face recognition methods, most of them treat the three channels separately. In this paper, a color face image is treated in a holistic manner by using the quaternion theory. We then propose a new algorithm for color face recognition, which uses the quaternion Zernike moment invariants and the quaternion BP neural network for the color face recognition. Experimental results on the Collection of Facial Images (Grimace) database, including major expression variation and considerable variation in head turn and tilt, show that the proposed method is better than the conventional ones in recognition rate.

License Plate Recognition Based on Neural Network Algorithm to Improve Research

2013 ◽  
Vol 860-863 ◽  
pp. 2892-2897 ◽  
Author(s):  
De Yong Liu ◽  
Hong Song ◽  
Quan Pan
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Bp Neural Network ◽  
High Speed ◽  
Recognition Rate ◽  
Training Transfer ◽  
License Plate ◽  
License Plate Recognition ◽  
The Real ◽  
Time Performance

with the development of intelligent transportation technology, which all countries are suitable for their own license plate recognition system is developed. But because of the CCD camera Angle problem will make license plate image tilt; Segmentation after do not match the characters in size and character discontinuity, led to license plate recognition rate is not high, speed slow, reduce the real-time performance of the system. In order to improve the rate of convergence, the recognition rate presents a license plate recognition algorithm based on BP neural network. First put the image correction, segmentation of character normalization processing and eliminate the unfavorable factors, then puts forward characteristics of characters input for training the BP neural network. By setting the network weights and training transfer function, improved algorithm to improve the recognition rate of the system, as well as the real-time performance.

Free Handwritten Numeral Recognition Based on BP Neural Network

2013 ◽  
Vol 850-851 ◽  
pp. 909-912
Author(s):  
Miao Chao Chen ◽  
Fang Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Matlab Simulation ◽  
Recognition Method ◽  
Simulation Experiments ◽  
Handwritten Numeral Recognition ◽  
Numeral Recognition ◽  
Important Branch ◽  
Application Prospects

Handwritten numeral recognition is an important branch in the field of pattern recognition, has broad application prospects. This article presents a method of using BP Neural Network to implement programme for recognition of free handwritten numerals. Scanned handwritten numeral image after preprocessing and feature extraction, classificated and recognized by the BP Neural Network. Through Matlab simulation experiments it shows that the recognition method is effective and has high recognition rate.

Pressure Prediction Technology of the Deep Strata Based on BP Neural Network

2010 ◽  
Vol 143-144 ◽  
pp. 28-31 ◽  
Author(s):  
Wei Li ◽  
Tie Yan ◽  
Ying Jie Liang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Gamma Ray ◽  
Forecast Model ◽  
Oil Field ◽  
The Neural Network ◽  
Natural Gamma ◽  
Gamma Ray Log ◽  
Deep Strata ◽  
Very High

. The accurate prediction of strata pressure is the base for safely, quality and efficiently drilling, decreasing hole problems and reasonable development of the reservoir. Because of the high cost, long cycle of the formation pressure measured method, which may influence the safety of drilling operation, thus a new method for predicting strata pressure, based on the BP neural network, is presented in this paper, and establishing process of the neural network forecast model are discussed in detail. This method takes the acoustic time, natural potential, natural gamma ray log data and pipe pressure test data as study sample, which has a very high accuracy. The paper predicts strata pressure of the Saertu oil field and Xingshugang oil field in Daqing, and the results show that relative error between the predicted data and experimental data is less than ±8.9%.

Research on Wear Debris Recognition Based on Patch Similarity of Anisotropic Diffusion and BP Neural Network

2012 ◽  
Vol 263-266 ◽  
pp. 2458-2461
Author(s):  
Jian Li Kang
Keyword(s):  
Neural Network ◽  
Convergence Rate ◽  
Bp Neural Network ◽  
Classification Accuracy ◽  
Wear Debris ◽  
Anisotropic Diffusion ◽  
Recognition Rate ◽  
Fast Learning ◽  
High Classification Accuracy ◽  
Feature Parameter

Wear debris recognition,which is based on patch similarity of anisotropic diffusion algorithm and BP neural network,is researched.At first, feature parameter refining of wear debris image,which was based on patch similarity of anisotropic diffusion algorithm feature parameter refining,was researched.Second,wear debirs classifier,which was based on the BP neural network and the first step,was researched.At last,with experiment results and experiment results analysis,the wear debris recogniton system in the paper is proved to be some merits,which include high classification accuracy,fast learning convergence rate and high recognition rate.

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