Pattern Classification of Surface Electromyography Based on AR Model and High-order Neural Network

Author(s):  
Zhizeng Luo ◽  
Fei Wang ◽  
Wenjie Ma
2005 ◽  
Vol 02 (04) ◽  
pp. 333-344 ◽  
Author(s):  
B. KARTHIKEYAN ◽  
S. GOPAL ◽  
S. VENKATESH

The quality of electrical insulation of any power apparatus is an indispensable requirement for its successful and reliable operation. Partial Discharge (PD) phenomenon serves as an effective Non Destructive Testing (NDT) technique and provides an index on the quality of the insulation. The innovative trend of using Artificial Neural Network (ANN) towards the classification of PD patterns is cogent and discernible. In this paper a novel method for the classification of the PD patterns using the original Probabilistic Neural Network (PNN) as well as its variation is elucidated. A preprocessing scheme that extracts pertinent features of PD from the raw data towards achieving a compact ANN has been employed. The classification of single-type insulation defects such as voids, surface discharges and corona has been taken up. The first part of the paper gives a brief on PD, various diagnostic techniques and interpretation. The second part deals with the theoretical concepts of PNN and its adaptive version. The last part provides details on various results and comparisons of the PNN and its adaptive version in PD pattern classification.


2013 ◽  
Vol 441 ◽  
pp. 738-741 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu

The network model of probabilistic neural network and its method of pattern classification and discrimination are first introduced in this paper. Then probabilistic neural network and three usually used back propagation neural networks are established through MATLAB7.0. The pattern classification of dots on a two-dimensional plane is taken as an example. Probabilistic neural network and improved back propagation neural networks are used to classify these dots respectively. Their classification results are compared with each other. The simulation results show that compared with back propagation neural networks, probabilistic neural network has simpler learning rules, faster training speed and it needs fewer training samples; the pattern classification method based on probabilistic neural network is very effective, and it is superior to the one based on back propagation neural networks in classifying speed, accuracy as well as generalization ability.


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