scholarly journals A Lung Sound Category Recognition Method Based on Wavelet Decomposition and BP Neural Network

2019 ◽  
Vol 15 (1) ◽  
pp. 195-207 ◽  
Author(s):  
Yan Shi ◽  
Yuqian Li ◽  
Maolin Cai ◽  
Xiaohua Douglas Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Wavelet Decomposition ◽  
Lung Sound ◽  
Recognition Method ◽  
Sound Category

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.

scholarly journals The Recognition Method of MQAM Signals Based on BP Neural Network and Bird Swarm Algorithm

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 36078-36086
Author(s):  
Chengchang Zhang ◽  
Sa Yu ◽  
Guojun Li ◽  
Yu Xu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Method ◽  
Swarm Algorithm ◽  
Bird Swarm Algorithm

scholarly journals Research on recognition method of cloud precipitation particle shape based on BP neural network

2021 ◽  
Vol 336 ◽  
pp. 06011
Author(s):  
Haonan Dong ◽  
Ruili Jiao ◽  
Minsong Huang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Particle Shape ◽  
Northern China ◽  
Recognition Method ◽  
Cloud Particle ◽  
Particle Images ◽  
Bp Neural Network Model

In order to solve the problem that the shape of cloud particle images measured by airborne cloud imaging probe (CIP) cannot be automatically recognized, this paper proposes an automatic recognition method of cloud and precipitation particle shape based on BP neural network. This method mainly uses a set of geometric parameters which can better describe the shape characteristics of cloud precipitation particles. Based on the cloud precipitation particle images measured by CIP in the precipitation stratiform clouds in northern China, a particle shape data training set and a testing set were constructed to train and verify the effect of the selected BP neural network model. The selected BP neural network model can classify the cloud particle image into tiny, column, needle, dendrite, aggregate, graupel, sphere, hexagonal and irregular. Utilizing the field campaign data measured by CIP, the habit identified results by the improved Holroyd method and by the selected BP neural network model were compared, which shows that the accuracy of BP neural network method is better than that of improved Holroyd method.

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.

A Recognition Method of License Plate Number Based on BP Neural Network

2014 ◽  
Vol 596 ◽  
pp. 422-426
Author(s):  
Bing Xiang Liu ◽  
Yan Hua Huang ◽  
Xu Dong Wu ◽  
Ying Xi Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Simulation Experiment ◽  
Processing Technique ◽  
Automatic Recognition ◽  
License Plate ◽  
License Plate Recognition ◽  
Recognition Method ◽  
Previous Period ◽  
Neural Network Algorithm

According to the current technological deficiency of license plate recognition, this paper uses digital graphic processing technique and BP Neural Network algorithm fusion to achieve automatic recognition of license plate. Input the image settled in the previous period in the trained BP neural network to obtain the final license plate character through simulation. The validity and feasibility of the algorithm can be verified through the simulation experiment of standard license plate image.

Drop Fingerprint Recognition Method Based on Cluster Analysis and BP Neural Network

2014 ◽  
Vol 543-547 ◽  
pp. 2099-2102 ◽  
Author(s):  
Qing Song ◽  
Dan Qing Du ◽  
Lu Yang ◽  
Gao Jie Meng ◽  
Xue Fei Mao
Keyword(s):  
Neural Network ◽  
Cluster Analysis ◽  
Bp Neural Network ◽  
Liquid Drop ◽  
Recognition Accuracy ◽  
Fingerprint Recognition ◽  
High Similarity ◽  
Recognition Method ◽  
Accuracy Rate ◽  
Accuracy Rates

The eigenvalues of some liquid drop fingerprints are of high similarity, which decreases the recognition accuracy rates of BP neural network. In order to solve this problem, recognition method based on cluster analysis and BP neural network is proposed in this paper. Cluster analysis is used to classify liquid samples according to the similarity of eigenvalues and narrow the recognition range for samples under study. The experimental results have proved that this method is able to increase the recognition accuracy rate from 83.42% to 99.83%.

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