A New Approach Based on S-transform for Discrimination and Classification of Inrush Current from Internal Fault Currents Using Probabilistic Neural Network

2010 ◽  
Vol 38 (10) ◽  
pp. 1194-1210 ◽  
Author(s):  
Z. Moravej ◽  
A. A. Abdoos ◽  
M. Sanaye-Pasand
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Inrush Current ◽  
New Approach ◽  
Fault Currents ◽  
Internal Fault ◽  
S Transform

Detection of inrush current using S-Transform and Probabilistic Neural Network

2010 ◽  
Author(s):  
G. Mokryani ◽  
M.-R. Haghifam ◽  
H. Latafat ◽  
P. Aliparast ◽  
A. Abdollahy
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Inrush Current ◽  
S Transform

scholarly journals Pattern Classification of Fabric Defects Using a Probabilistic Neural Network and Its Hardware Implementation using the Field Programmable Gate Array System

2017 ◽  
Vol 25 (0) ◽  
pp. 42-48 ◽  
Author(s):  
Abul Hasnat ◽  
Anindya Ghosh ◽  
Amina Khatun ◽  
Santanu Halder
Keyword(s):  
Neural Network ◽  
Hardware Implementation ◽  
Probabilistic Neural Network ◽  
Clock Period ◽  
Data Set ◽  
Gate Arrays ◽  
Hardware System ◽  
Field Programmable ◽  
Programmable Gate Arrays

This study proposes a fabric defect classification system using a Probabilistic Neural Network (PNN) and its hardware implementation using a Field Programmable Gate Arrays (FPGA) based system. The PNN classifier achieves an accuracy of 98 ± 2% for the test data set, whereas the FPGA based hardware system of the PNN classifier realises about 94±2% testing accuracy. The FPGA system operates as fast as 50.777 MHz, corresponding to a clock period of 19.694 ns.

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