Title :
A Vision-Based Inspection System using Fuzzy Rough Neural Network Method
Author :
Li, Meng-xin ; Wu, Cheng-dong ; Jin, Feng
Author_Institution :
Fac. of Inf. & Control Eng., Shenyang Jianzhu Univ.
Abstract :
A vision-based inspection method based on rough set theory, fuzzy set and neural network algorithm is presented. The rough set method is proposed to remove redundant features for its data analysis and processing. The reduced data is fuzzified to represent the feature data in a more suitable form for input to a BP network classifier. The BP neural classifier is considered the most popular, effective and easy-to-learn model for complex, multi-layered network. By the experiment research, the hybrid method shows good classification accuracy and short running time, which are better than the results using BP network and neural network with fuzzy input
Keywords :
automatic optical inspection; backpropagation; computer vision; fuzzy set theory; milling; neural nets; pattern classification; rough set theory; wood processing; backpropagation network classifier; data analysis; fuzzy rough neural network algorithm; fuzzy set theory; rough set theory; vision-based inspection system; Control engineering; Cybernetics; Data analysis; Electronic mail; Fuzzy control; Fuzzy neural networks; Fuzzy set theory; Fuzzy systems; Humans; Information systems; Inspection; Machine learning; Neural networks; Set theory; Vision-based inspection; classification; fuzzy input; neural Network; rough set;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.258431
