Title :
Adaptive fuzzy associative memory for online quality control
Author :
Shahir, Shahed ; Chen, Xiang
Author_Institution :
Dept. of Electr. & Comput. Eng., Windsor Univ., Ont., Canada
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;
Conference_Titel :
System Theory, 2003. Proceedings of the 35th Southeastern Symposium on
Print_ISBN :
0-7803-7697-8
DOI :
10.1109/SSST.2003.1194591
