The Design of the BP Neural Network Character Recognition in Matlab Environment

2014 ◽  
Vol 1006-1007 ◽  
pp. 1117-1120 ◽  
Author(s):  
Wen Jie Li ◽  
Jie Zhang ◽  
Ke Lun Tian ◽  
Hai Yan Sun
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Character Recognition ◽  
Test Form ◽  
Identification Method ◽  
Specific Implementation ◽  
Implementation Steps

This paper is based on the BP neural network, the identification method of character and the specific implementation steps were designed. Moreover, the method through the test form has been proved. The accuracy of character recognition is higher.

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.

Study of Digital Character Recognition Based on BP Neural Networks

2013 ◽  
Vol 333-335 ◽  
pp. 856-859 ◽  
Author(s):  
Shuai Yuan ◽  
Guo Yun Zhang ◽  
Jian Hui Wu ◽  
Long Yuan Guo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Character Recognition ◽  
Back Propagation ◽  
Solution Space ◽  
Training Sample ◽  
Back Propagation Algorithm ◽  
Sample Object ◽  
Training Error ◽  
Hidden Layer

A digital character recognition method is presented based on BP Neural Network. This paper preprocesses the digital character image and extracts character feature, then uses BP Neural Network to recognize digital character. Back Propagation algorithm seeks network weights to minimize training error in the solution space. A network with hidden layer is created at first, then an input sample vector is sent to network input terminal and the square error E between output values and training sample object output values is calculated. Above process is repeated for input samples of training sets until the error is reduced within the limits of the threshold. The results show that the method presented has good accuracy, quick speed and strong robustness for realtime application.

Study on the Identification Method of Tool Wear State Based on BP Neural Network Optimized by Genetic Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 2458-2461 ◽  
Author(s):  
Zheng Qiang Li ◽  
Peng Nie ◽  
Shu Guo Zhao ◽  
Zhang Shun Ding
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Acoustic Emission ◽  
Tool Wear ◽  
Bp Neural Network ◽  
Solution Space ◽  
Optimal Solutions ◽  
Accurate Identification ◽  
Identification Method ◽  
Singular Spectrum

According to the un-stationary feature of the acoustic emission signals of tool wear, a tool wear state identification method based on genetic algorithm and BP neural network was proposed. The method reconstructed the acoustic emission signals and calculated the singular spectrum. And the feature vectors were reconstructed based on the singular spectrum. BP neural network was optimized by genetic algorithm. The weights of BP neural network and the thresholds were optimized originally to get more optimal solutions in solution space. Then the more optimal solutions were put into BP neural network to identify the tool wear state by the optimized classification machine. The study indicated that this method can make an accurate identification of tool wear state and should be widely used.

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