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%.

Improvement of Waveform Analysis Method Based on Polynomial Fitting for Liquid Drop Fingerprint

2014 ◽  
Vol 945-949 ◽  
pp. 2093-2096
Author(s):  
Qing Song ◽  
Dan Qing Du ◽  
Lu Yang ◽  
Gao Jie Meng ◽  
Xue Fei Mao
Keyword(s):  
Bp Neural Network ◽  
Liquid Drop ◽  
Recognition Accuracy ◽  
Smooth Curve ◽  
Waveform Analysis ◽  
Maximum Difference ◽  
Analysis Method ◽  
Accuracy Rate ◽  
Polynomial Fitting ◽  
Waveform Fitting

Waveform analysis method is widely used for feature extraction of liquid drop fingerprint, but it is easily affected by noise. To solve this problem, waveform fitting and analysis method based on polynomial fitting is proposed, though which the waveform of liquid drop fingerprint is fitted to be a smooth curve. Experimental results show that waveform fitting and analysis method is able to reduce the standard deviation and maximum difference of eigenvectors from the same kinds of liquid, and thus increase the recognition accuracy rate of 10 kinds of water based on BP neural network from 86.5% to 100%.

Study on Recognition Method of Ferrographic Debris Based on Neural Network

2013 ◽  
Vol 774-776 ◽  
pp. 1508-1511
Author(s):  
Ya Bin Cao
Keyword(s):  
Neural Network ◽  
Neural Network Model ◽  
Digital Image ◽  
Bp Neural Network ◽  
Recognition Accuracy ◽  
Characteristic Parameter ◽  
Field Of View ◽  
Recognition Method ◽  
Lighting Conditions ◽  
Bp Neural Network Model

A new method is proposed in which three images of the same field of view under different lighting conditions were analysed comprehensively. On the basis of pretreatment of debris digital image and extraction of characteristic parameter of debrises, the debrises were recognized by establishing the BP neural network model. The results showed that the recognition accuracy of debrises could reach 85.6% with this method.

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.

scholarly journals Fast identification method for express end sorting label code based on convolutional recurrent neural network

2020 ◽  
Vol 14 (8) ◽  
pp. 1689-1697
Author(s):  
Haiyan Du ◽  
Chunxue Wu ◽  
Yan Wu ◽  
Ren Han ◽  
Xiao Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Processing Speed ◽  
Recognition Accuracy ◽  
Code Region ◽  
Digital Information ◽  
Coding Region ◽  
Recognition Method ◽  
Sequence Recognition ◽  
Sorting Process

Abstract In the automatic sorting process of express, the express end sorting label code is used to indicate that the express is dispatched to a specific address by a specific courier. Since there are many areas on the express bill containing digital information, some areas may be improperly photographed, etc. The difficulty in positioning and recognizing the express end sorting label code region is increased. To solve this problem, this paper proposes an express end sorting label code recognition method with convolutional recurrent neural network for the code specification, which has certain versatility. In order to improve the overall code recognition speed, this paper optimizes the traditional digital recognition method, removes the original segmentation operation of the character and recognizes the code as sequence recognition. Firstly, the coding region is located, and then, the express end sorting label code is recognized by the convolutional recurrent neural network. In order to test the experimental performance, this paper tests on Free-Type dataset and SUN-synthesized dataset. The experimental results show that the proposed method improves the recognition accuracy and processing speed of the express end sorting label code.

scholarly journals Human Body Mixed Motion Pattern Recognition Method Based on Multi-Source Feature Parameter Fusion

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 537 ◽  
Author(s):  
Jiyuan Song ◽  
Aibin Zhu ◽  
Yao Tu ◽  
Yingxu Wang ◽  
Muhammad Affan Arif ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Lower Extremity ◽  
Human Body ◽  
Recognition Accuracy ◽  
Screening Method ◽  
Motion Pattern ◽  
Recognition Method ◽  
Motion State ◽  
Motion Mode

Aiming at the requirement of rapid recognition of the wearer’s gait stage in the process of intelligent hybrid control of an exoskeleton, this paper studies the human body mixed motion pattern recognition technology based on multi-source feature parameters. We obtain information on human lower extremity acceleration and plantar analyze the relationship between these parameters and gait cycle studying the motion state recognition method based on feature evaluation and neural network. Based on the actual requirements of exoskeleton per use, 15 common gait patterns were determined. Using this, the studies were carried out on the time domain, frequency domain, and energy feature extraction of multi-source lower extremity motion information. The distance-based feature screening method was used to extract the optimal features. Finally, based on the multi-layer BP (back propagation) neural network, a nonlinear mapping model between feature quantity and motion state was established. The experimental results showed that the recognition accuracy in single motion mode can reach up to 98.28%, while the recognition accuracy of the two groups of experiments in mixed motion mode was found to be 92.7% and 97.4%, respectively. The feasibility and effectiveness of the model were verified.

scholarly journals Music emotion recognition algorithm based on BP neural network

2022 ◽  
Vol 355 ◽  
pp. 03021
Author(s):  
Xu Liu ◽  
Pingxiao Ge
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Bp Neural Network ◽  
Recognition Accuracy ◽  
Recognition Algorithm ◽  
Expected Effect ◽  
Hidden Layer

Music plays a very important role in animation production. Because it could better express the emotion of the character, this paper uses BP neural network to identify the music emotion. This paper first introduced the structure of BP neural network. Then, the parameters and structure of the network were designed according to the category of music emotion. Finally, a three-layer BP neural network with 5 input nodes, 13 hidden layer nodes and 4 output nodes was constructed and applied to music emotion recognition. The recognition accuracy was 85.02%, which basically met the requirements of music emotion recognition and achieves the expected effect.

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