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

被引：1
|
作者：
Castillo, O [1 ]
Cardona, R [1 ]
Melin, P [1 ]
机构：
[1] Tujuana Inst Technol, Dept Comp Sci, Chula Vista, CA 91909 USA
来源：
PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3 | 2002年
关键词：
D O I：
10.1109/IJCNN.2002.1007462
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：2081 / 2085
页数：3
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