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.

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.

Fusion of Image Contour Moments and Fourier Descriptors for the Hand Gesture Recognition

2012 ◽  
Vol 433-440 ◽  
pp. 5188-5192
Author(s):  
Hai Long Lei ◽  
Sheng Yang
Keyword(s):  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Fourier Descriptor ◽  
Bp Network ◽  
Moment Invariant ◽  
Hand Shape ◽  
Image Contour

Hand is a highly variable organ and hand features are easily affected by environmental factors. Considering the characteristics of hand gesture, a novel hand gesture recognition algorithm based on hybrid moments is presented. First, According to the color cue, the hand shape is available to extract from the complicated background, then the contour moment invariant and Fourier Descriptor are extracted and fused into a hybrid feature, finally the hybrid feature are put into the BP network to identity. The experimental results show that the method has better robustness and higher recognition rate.

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 APPLICATION OF BACK PROPAGATION NEURAL NETWORK IN SPORTS FATIGUE INDICATORS

2021 ◽  
Vol 27 (3) ◽  
pp. 249-252
Author(s):  
Xiaoli Wang ◽  
Chunmin Dai
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Principal Component ◽  
Level Of Evidence ◽  
Bp Network ◽  
Training Time ◽  
Emg Signal ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Learning Machine

ABSTRACT Introduction High-intensity rehabilitation training will produce exercise fatigue. Objective A backpropagation (BP) network neural algorithm is proposed to predict sports fatigue based on electromyography (EMG) signal images. Methods The principal component analysis algorithm is used to reduce the dimension of EMG signal features. The knee joint angle is estimated by the regularized over-limit learning machine algorithm and the BP neural network algorithm. Results The RMSE value of the regularized over-limit learning machine algorithm is lower than that of the BP neural network algorithm. At the same time, the ρ value of the regularized over-limit learning machine algorithm is closer to 1, indicating its higher accuracy. Conclusions The model training time of the regularized over-limit learning machine algorithm has been greatly reduced, which improves efficiency. Level of evidence II; Therapeutic studies - investigation of treatment results.

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.

scholarly journals Surface EMG-Based Instantaneous Hand Gesture Recognition Using Convolutional Neural Network with the Transfer Learning Method

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2540
Author(s):  
Zhipeng Yu ◽  
Jianghai Zhao ◽  
Yucheng Wang ◽  
Linglong He ◽  
Shaonan Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Gesture Recognition ◽  
Recognition System ◽  
Surface Emg ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Training Time ◽  
Generalization Ability

In recent years, surface electromyography (sEMG)-based human–computer interaction has been developed to improve the quality of life for people. Gesture recognition based on the instantaneous values of sEMG has the advantages of accurate prediction and low latency. However, the low generalization ability of the hand gesture recognition method limits its application to new subjects and new hand gestures, and brings a heavy training burden. For this reason, based on a convolutional neural network, a transfer learning (TL) strategy for instantaneous gesture recognition is proposed to improve the generalization performance of the target network. CapgMyo and NinaPro DB1 are used to evaluate the validity of our proposed strategy. Compared with the non-transfer learning (non-TL) strategy, our proposed strategy improves the average accuracy of new subject and new gesture recognition by 18.7% and 8.74%, respectively, when up to three repeated gestures are employed. The TL strategy reduces the training time by a factor of three. Experiments verify the transferability of spatial features and the validity of the proposed strategy in improving the recognition accuracy of new subjects and new gestures, and reducing the training burden. The proposed TL strategy provides an effective way of improving the generalization ability of the gesture recognition system.

Power Transform Fault Diagnosis Based on the Adaptive Mutation Particle Swarm Optimization

2013 ◽  
Vol 756-759 ◽  
pp. 3804-3808
Author(s):  
Zhi Mei Duan ◽  
Jia Tang Cheng
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Adaptive Mutation ◽  
Bp Network ◽  
Swarm Optimization ◽  
Fault Recognition

In order to improve the accuracy of fault diagnosis of power transformer, in this paper, a method is proposed that optimize the weight of BP neural network by adaptive mutation particle swarm optimization (AMPSO). According to the characteristic of transformer fault, the optimized neural network is used to diagnose fault of the power transformer. Individual particles action is amended by this algorithm and local minima problems of the standard PSO and BP network are overcooked. The experimental results show that, the method can classify transformer faults, and effectively improve the fault recognition rate.

AUTOMATED RECOGNITION OF SINGLE & HYBRID POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM BASED SUPPORT VECTOR MACHINE

2016 ◽  
Vol 79 (1) ◽  
Author(s):  
Suhail Khokhar ◽  
A. A. Mohd Zin ◽  
M. A. Bhayo ◽  
A. S. Mokhtar
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Classification Performance ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network

The monitoring of power quality (PQ) disturbances in a systematic and automated way is an important issue to prevent detrimental effects on power system. The development of new methods for the automatic recognition of single and hybrid PQ disturbances is at present a major concern. This paper presents a combined approach of wavelet transform based support vector machine (WT-SVM) for the automatic classification of single and hybrid PQ disturbances. The proposed approach is applied by using synthetic models of various single and hybrid PQ signals. The suitable features of the PQ waveforms were first extracted by using discrete wavelet transform. Then SVM classifies the type of PQ disturbances based on these features. The classification performance of the proposed algorithm is also compared with wavelet based radial basis function neural network, probabilistic neural network and feed-forward neural network. The experimental results show that the recognition rate of the proposed WT-SVM based classification system is more accurate and much better than the other classifiers. 

scholarly journals Social Media Analysis Using Probabilistic Neural Network Algorithm to Know Personality Traits

Jurnal INFORM ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 61-64
Author(s):  
Mohammad Zoqi Sarwani ◽  
Dian Ahkam Sani
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Probabilistic Neural Network ◽  
Media Analysis ◽  
Training Data ◽  
The Internet ◽  
Accuracy Score ◽  
Network Algorithm ◽  
The Social ◽  
Neural Network Algorithm

The Internet creates a new space where people can interact and communicate efficiently. Social media is one type of media used to interact on the internet. Facebook and Twitter are one of the social media. Many people are not aware of bringing their personal life into the public. So that unconsciously provides information about his personality. Big Five personality is one type of personality assessment method and is used as a reference in this study. The data used is the social media status from both Facebook and Twitter. Status has been taken from 50 social media users. Each user is taken as a text status. The results of tests performed using the Probabilistic Neural Network algorithm obtained an average accuracy score of 86.99% during the training process and 83.66% at the time of testing with a total of 30 training data and 20 test data.

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