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

被引：0

作者：

Hasnat A. ^{[1
]}

Ghosh A. ^{[1
]}

Khatun A. ^{[2
]}

Halder S. ^{[3
]}

机构：

[1] Government College of Engineering & Textile Technology, Berhampore, West Bengal

[2] Jadavpur University, Kolkata, West Bengal

[3] Government Govt. College of Engineering and Leather Technology, Kolkata, West Bengal

来源：

| 1600年 / Lukasiewicz Research Network - Institute of Biopolymers and Chemical Fibres卷 / 25期

关键词：

Classification; Fabric defect; Field programmable gate arrays; Probabilistic neural network; Radial basis function;

D O I：

10.5604/01.3001.0010.1709

中图分类号：

学科分类号：

摘要：

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.

引用

页码：42 / 48

页数：6

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