Pattern Classification of Fabric Defects Using a Probabilistic Neural Network and Its Hardware Implementation using the Field Programmable Gate Array System
Research and development
Authors:
- Hasnat Abul
Government College of Engineering & Textile Technology, Berhampore, India - Ghosh Anindya (j/w)
- Khatun Amina
Jadavpur University, Kolkata, India - Halder Santanu
Government Govt. College of Engineering & Leather Technology, Kolkata, India
Nr DOI: 10.5604/01.3001.0010.1709
 Full text | references | Abstract: 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. |
Tags:
classification, fabric defect, field programmable gate arrays, radial basis function, probabilistic neural network.
Citation:
Hasnat A, Ghosh A, Khatun A, Halder S. Pattern Classification of Fabric Defects Using a Probabilistic Neural Network and Its Hardware Implementation using the Field Programmable Gate Array System. FIBRES & TEXTILES in Eastern Europe 2017; 25, 1(121): 42-48. DOI: 10.5604/01.3001.0010.1709