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

Research and development

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Nr DOI: 10.5604/01.3001.0010.1709

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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

Published in issue no 1 (121) / 2017, pages 42–48.

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