Title :
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
fDate :
6/24/1905 12:00:00 AM
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;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007462