Title :
A neuro-fuzzy approach in parts clustering
Author_Institution :
Dept. of Ind. Eng. & Manage., Minghsin Inst. of Technol., Hsingchu, Taiwan
Abstract :
In the feature-based clustering system, conversion from part feature to crisp codes is a conventional procedure in part clustering. However, part characteristics are reduced in the procedure especially for fuzzy and interval attributes. To remedy the shortages, a self-organizing map network with fuzzy weights is proposed. By taking the linguistic representation capabilities of fuzzy theory and the clustering abilities of self-organizing map (SOM) networks, the proposed approach is able to deal with not only crisp attributes but also interval attributes as well as fuzzy attributes. Due to the fuzzy data and fuzzy weights between the input layer and output layer, a learning rule is presented. The influence of the three parameters in the network is discussed
Keywords :
fuzzy neural nets; learning (artificial intelligence); pattern recognition; self-organising feature maps; clustering; crisp attributes; feature-based clustering system; fuzzy attributes; fuzzy weights; interval attributes; learning rule; linguistic representation; neuro-fuzzy approach; parts clustering; self-organizing map network; Engineering management; Fuzzy neural networks; Humans; Industrial engineering; Machining; Moon; Neural networks; Random number generation; Self organizing feature maps; Technology management;
Conference_Titel :
Fuzzy Information Processing Society, 2000. NAFIPS. 19th International Conference of the North American
Conference_Location :
Atlanta, GA
Print_ISBN :
0-7803-6274-8
DOI :
10.1109/NAFIPS.2000.877406
