The Application of the Fuzzy ART Neural Network Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 1783-1786 ◽  
Author(s):  
Ming Gu
Keyword(s):  
Neural Network ◽  
Simulation Experiment ◽  
Recognition Rate ◽  
Learning Rule ◽  
Recognition Algorithm ◽  
Fuzzy Art ◽  
Fast Learning ◽  
Average Recognition Rate ◽  
Neural Network Algorithm ◽  
Better Than

An algorithm based on fuzzy ART neural network which can deal with online-learning and recognition of the known and unknown faces at the same time was designed and realized. Based on structure and learning rule of the fuzzy ART system, face recognition algorithm was designed. The simulation experiment results show that average recognition rate of not fast learning is better than fast learning. Not fast learning is accepted to get 89.83% online and 99.42% offline recognition rate.

The Recognition and Research of Improved Probabilistic Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 2819-2824
Author(s):  
Xiao Jing Shang
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Bp Network ◽  
Training Time ◽  
Neural Network Structure ◽  
Neural Network Algorithm ◽  
Better Than

Probabilistic neural network compared with the traditional BP neural network structure is simpler and it is faster to be identificated, so it is widely used in the field of pattern recognition. This paper is mainly focused on similar gesture recognition research, propose an probabilistic neural network gesture recognition algorithm. The simulation results show that the improved probabilistic neural network algorithm on the recognition rate and training time is better than the traditional BP network.

scholarly journals A Convolutional Neural Network Algorithm for the Optimization of Emergency Nursing Rescue Efficiency for Critical Patients

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Xueyan Chen ◽  
Xiaofei Zhong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Recognition Rate ◽  
Recognition System ◽  
Emergency Nursing ◽  
Network Algorithm ◽  
Average Recognition Rate ◽  
Neural Network Algorithm ◽  
Critical Patients

In order to help pathologists quickly locate the lesion area, improve the diagnostic efficiency, and reduce missed diagnosis, a convolutional neural network algorithm for the optimization of emergency nursing rescue efficiency of critical patients was proposed. Specifically, three convolution layers and convolution kernels of different sizes are used to extract the features of patients’ posture behavior, and the classifier of patients’ posture behavior recognition system is used to learn the feature information by capturing the nonlinear relationship between the features to achieve accurate classification. By testing the accuracy of patient posture behavior feature extraction, the recognition rate of a certain action, and the average recognition rate of all actions in the patient body behavior recognition system, it is proved that the convolution neural network algorithm can greatly improve the efficiency of emergency nursing. The algorithm is applied to the patient posture behavior detection system, so as to realize the identification and monitoring of patients and improve the level of intelligent medical care. Finally, the open source framework platform is used to test the patient behavior detection system. The experimental results show that the larger the test data set is, the higher the accuracy of patient posture behavior feature extraction is, and the average recognition rate of patient posture behavior category is 97.6%, thus verifying the effectiveness and correctness of the system, to prove that the convolutional neural network algorithm has a very large improvement of emergency nursing rescue efficiency.

scholarly journals Recognition Algorithm of Popular Elements of Ethnic Minority Traditional Clothing Based on PCA

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hu Juan
Keyword(s):  
Ethnic Minority ◽  
National Culture ◽  
Euclidean Distance ◽  
Recognition Rate ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Pca Method ◽  
Neural Network Algorithm ◽  
Matching Degree ◽  
Better Than

Image recognition of ethnic minority costumes is helpful for people to understand, carry forward, and inherit national culture. Taking the minority clothing image as the research object, the image enhancement and threshold segmentation are completed; the principal component features of the minority clothing image are extracted by PCA method; and the image matching degree is obtained according to the principle of minimizing the Euclidean distance. Finally, the calculation process of the PCA method is optimized by a wavelet transform algorithm to realize the recognition of popular elements of minority traditional clothing. The comparative experimental results show that the PCA + BP neural network algorithm is better than the other two recognition algorithms in recognition rate and recognition time.

scholarly journals Eye Movement Prediction Based on Adaptive BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yushou Tang ◽  
Jianhuan Su
Keyword(s):  
Neural Network ◽  
Eye Movements ◽  
Eye Movement ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Eye Movement Tracking ◽  
Neural Network Algorithm ◽  
Real World Datasets ◽  
Eye Movements During Reading

This paper uses adaptive BP neural networks to conduct an in-depth examination of eye movements during reading and to predict reading effects. An important component for the implementation of visual tracking systems is the correct detection of eye movement using the actual data or real-world datasets. We propose the identification of three typical types of eye movements, namely, gaze, leap, and smooth navigation, using an adaptive BP neural network-based recognition algorithm for eye movement. This study assesses the BP neural network algorithm using the eye movement tracking sensors. For the experimental environment, four types of eye movement signals were acquired from 10 subjects to perform preliminary processing of the acquired signals. The experimental results demonstrate that the recognition rate of the algorithm provided in this paper can reach up to 97%, which is superior to the commonly used CNN algorithm.

COMPARISON OF NEURAL NETWORK AND K-NN CLASSIFICATION METHODS IN VOWEL AND PATELLAR SUBLUXATION IMAGE RECOGNITIONS

1993 ◽  
Vol 07 (04) ◽  
pp. 775-782 ◽  
Author(s):  
EUNG-KYEU KIM ◽  
JIAN-TONG WU ◽  
SHINICHI TAMURA ◽  
YOSHINOBU SATO ◽  
ROBERT CLOSE ◽  
...  
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Average Recognition Rate ◽  
Patellar Subluxation ◽  
Voice Data ◽  
Neighbor Classification ◽  
Better Than

We make a comparision of classification ability between BPN (BackPropagation Neural Network) and k-NN (k-Nearest Neighbor) classification methods. Voice data and patellar subluxation images are used. The result was that the average recognition rate of BPN was 9.2 percent higher than that of the k-NN classification method. Although k-NN classification is simple in theory, classification time was fairly long. Therefore, it seems that real time recognition is difficult. On the other hand, the BPN method has a long learning time but a very short recognition time. Especially if the number of dimensions of the samples is large, it can be said that BPN is better than k-NN in classification ability.

scholarly journals UNSTABLE MORSE CODE RECOGNITION SYSTEM WITH EXPERT-GATING NEURAL NETWORK

2002 ◽  
Vol 14 (01) ◽  
pp. 12-19 ◽  
Author(s):  
DUU-TONG FUH ◽  
CHING-HSING LUO
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Disabled Persons ◽  
Single Chip ◽  
Morse Code ◽  
Average Recognition Rate ◽  
Rate Test

The standard Morse code defines the tone ratio (dash/dot) and the silent ratio (dash-space/dotspace) as 3:1. Since human typing ratio can't keep this ratio precisely and the two ratios —tone ratio and silent ratio—are not equal, the Morse code can't be recognized automatically. The requirement of the standard ratio is difficult to satisfy even for an ordinary person. As for the unstable Morse code typing pattern, the auto-recognition algorithms in the literature are not good enough in applications. The disabled persons usually have difficulty in maintaining a stable typing speeds and typing ratios, we therefore adopted an Expert-Gating neural network model to implement in single chip and recognize online unstable Morse codes. Also, we used another method—a linear back propagation recalling algorithm, to implement in single chip and recognize unstable Morse codes. From three person tests: Test one is a cerebral palsy; Test two is a beginner: Test three is a skilled expert, we have the results: in the experiment of test one, we have 91.15% (use 6 characters average time series as thresholds) and 91.54% (learning 26 characters) online average recognition rate; test two have 95.77% and 96.15%, and test three have 98.46% and 99.23% respectively. As for linear back propagation recalling method online recognized rate, we have the results from test one: 92.31% online average recognition rate; test two: 96.15%; and test three 99.23% respectively. So, we concluded: The Expert-Gating neural network and the linear back propagation recalling algorithm have successfully overcome the difficulty of analyzing a severely online unstable Morse code time series and successfully implement in single chip to recognize online unstable Morse code.

A Feature Recognition Approach with Fuzzy ART Neural Network

2011 ◽  
Vol 279 ◽  
pp. 406-411
Author(s):  
Cong Lu ◽  
Jun Zha
Keyword(s):  
Neural Network ◽  
Feature Recognition ◽  
Boundary Representation ◽  
Solid Model ◽  
Fuzzy Art ◽  
Fast Learning ◽  
Key Technologies ◽  
Learning Modes ◽  
Slow Learning

This paper proposes a feature recognition approach from a boundary representation solid model with Fuzzy ART neural network. To recognize the feature efficiently, some key technologies in Fuzzy ART neural network are used. The influence of the vigilance parameter on feature recognition is studied, and two learning modes, fast learning and slow learning are adopted and compared in feature recognition. Finally, a case study is given to verify the proposed approach.

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.

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.

Speech Recognition Algorithm Based on Empirical Mode Decomposition and RBF Neural Network

2013 ◽  
Vol 831 ◽  
pp. 465-469
Author(s):  
Wei Wei Shi ◽  
Wei Hua Xiong ◽  
Wei Chen
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Empirical Mode Decomposition ◽  
Rbf Neural Network ◽  
Recognition Rate ◽  
Good Method ◽  
Recognition Algorithm ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Learning Speed

This paper presents a novel method of the speech recognition in combining the empirical mode decomposition with radical basis function neural network. Speech signals which pretreated are decomposed by empirical mode decomposition to get a set of intrinsic mode functions. It extracts mel frequency cepstrum coefficient from intrinsic mode function. Features parameters are made up of the coefficients. For BP Neural Network, RBF Neural Network has advantages on approximating ability and learning speed. So using RBF Neural Network as a recognition model is a good method. Experiments show that this new method has good robustness and adaptability. The speech recognition rate of this method reach ninety-one percents accurately under no noise environment. Speech signal recognition is feasible and effective in noisy environment.

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