Pattern classification of fabric defects using a probabilistic neural network and its hardware implementation using the field programmable gate array system

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. © 2017, Institute of Biopolymers and Chemical Fibres. All rights reserved.