Application of the pairwise variability index of speech rhythm with particle swarm optimization to the classification of native and non-native accents

2018 ◽  
Vol 48 ◽  
pp. 67-79 ◽  
Author(s):  
Soumaya Gharsellaoui ◽  
Sid Ahmed Selouani ◽  
Wladyslaw Cichocki ◽  
Yousef Alotaibi ◽  
Adel Omar Dahmane
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Speech Rhythm ◽  
Swarm Optimization ◽  
Variability Index

scholarly journals The application of particle swarm optimization using neural network to optimize classification of employee performance assessment

2019 ◽  
Vol 1175 ◽  
pp. 012067
Author(s):  
Mohammad Humam ◽  
Oman Somantri ◽  
Maylane Boni Abdillah ◽  
Syaefani Arif Romadhon ◽  
Mohammad Khambali ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Performance Assessment ◽  
Employee Performance ◽  
Particle Swarm ◽  
Swarm Optimization

New Research on Identification and Classification of Grid Power Quality Disturbances

2014 ◽  
Vol 986-987 ◽  
pp. 1431-1434
Author(s):  
Ning Xia Yang ◽  
Mao Fa Gong ◽  
Xiao Fei Wang ◽  
Hui Ting Ge ◽  
Yu Qing Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Wavelet Transform ◽  
Power Quality ◽  
Particle Swarm ◽  
Interference Signal ◽  
Swarm Optimization ◽  
Frequency Information ◽  
Power Quality Disturbances

To improve accuracy and speed of recognising and classifying grid power quality disturbances, this paper presents a new method which combines complex wavelet transform and particle swarm optimization (PSO) neural network to identify and classify the disturbance . This method extract both amplitude-frequency and phase frequency information of the interference signal to make up for the lack of traditional wavelet transform which only extract the amplitude-frequency information. And on this basis, using particle swarm optimization, we seek the optimal solution of neural network weights and thresholds for the identification and classification of power quality. The MATLAB simulation result has verified the accuracy and rapidity of this method compared with the traditional method .

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