A hybrid approach for automated quality control combining learning vector quantization neural networks and fuzzy logic

Author

Castillo, Oscar ; Cardona, Raul ; Melin, Patricia

Author_Institution

Dept. of Comput. Sci., Tijuana Inst. of Technol., Chula Vista, CA, USA

Volume

3

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

2081

Lastpage

2085

Abstract

We describe in this paper a new hybrid intelligent approach for automated quality control combining Learning Vector Quantization (LVQ) and fuzzy logic. In our approach, LVQ neural networks are used for image processing and classification. Also, a set of fuzzy rules is used for solving the problem of automating the decision making for quality control. The fuzzy system contains the expert knowledge for quality evaluation. The new approach has been tested with the specific case of automating the quality control of tomato in a food processing plant with excellent results

Keywords

food processing industry; fuzzy logic; image classification; image processing; learning (artificial intelligence); neural nets; quality control; vector quantisation; automated quality control; food processing plant; fuzzy logic; fuzzy rules expert knowledge; hybrid intelligent approach; image classification; image processing; learning vector quantization neural networks; Automatic testing; Decision making; Fuzzy control; Fuzzy logic; Fuzzy sets; Fuzzy systems; Image processing; Neural networks; Quality control; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1007462

Filename

1007462