Adaptive fuzzy associative memory for online quality control

Author

Shahir, Shahed ; Chen, Xiang

Author_Institution

Dept. of Electr. & Comput. Eng., Windsor Univ., Ont., Canada

fYear

2003

fDate

16-18 March 2003

Firstpage

357

Lastpage

361

Abstract

In this paper, an online quality inspection is presented based on the adaptive fuzzy associative memory (AFAM) theory. The AFAM along with vision technology enables us to inspect the quality of each component online. Throughout the process, four different types of classification exist, namely, desired, stretched, squeezed and deformed foam barrier. The learning vector quantization (LVQ) is applied to train the system based on the defined clusters according to the trainees. After ending a course of training, a bank of fuzzy associative memory (BFAM) is constructed. To perform online quality inspection, the composition applies to the input fuzzy vector and BFAM.

Keywords

automatic optical inspection; computer vision; fuzzy neural nets; fuzzy set theory; image classification; learning (artificial intelligence); quality control; vector quantisation; adaptive fuzzy associative memory; fuzzy database; fuzzy search engine; fuzzy set theory; image classification; learning vector quantization; neural network; quality control; Adaptive control; Associative memory; Automotive engineering; Fuzzy control; Fuzzy logic; Inspection; Neural networks; Production; Programmable control; Quality control;

fLanguage

English

Publisher

ieee

Conference_Titel

System Theory, 2003. Proceedings of the 35th Southeastern Symposium on

ISSN

0094-2898

Print_ISBN

0-7803-7697-8

Type

conf

DOI

10.1109/SSST.2003.1194591

Filename

1194591