TY - GEN
T1 - A memory fuzzy learning for uncertainty management in disassembly
AU - Tang, Ying
PY - 2006/12/1
Y1 - 2006/12/1
N2 - Disassembly is of increasing importance in material and product recovery. However, this process is fraught with many uncertainties (e.g., variations in product structure and condition). In our previous work [11], such dynamics in disassembly process planning were addressed through an adaptive fuzzy system and associated algorithms. Building upon the work, this paper presents an enhanced fuzzy learning algorithm with variable memory length to ensure the robustness of the adaptation procedure. The proposed methodology and algorithm are illustrated via the disassembly of a batch of flashlights in a prototypical disassembly system.
AB - Disassembly is of increasing importance in material and product recovery. However, this process is fraught with many uncertainties (e.g., variations in product structure and condition). In our previous work [11], such dynamics in disassembly process planning were addressed through an adaptive fuzzy system and associated algorithms. Building upon the work, this paper presents an enhanced fuzzy learning algorithm with variable memory length to ensure the robustness of the adaptation procedure. The proposed methodology and algorithm are illustrated via the disassembly of a batch of flashlights in a prototypical disassembly system.
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M3 - Conference contribution
AN - SCOPUS:34250218248
SN - 1424400651
SN - 9781424400652
T3 - Proceedings of the 2006 IEEE International Conference on Networking, Sensing and Control, ICNSC'06
SP - 522
EP - 527
BT - Proceedings of the 2006 IEEE International Conference on Networking, Sensing and Control, ICNSC'06
T2 - 2006 IEEE International Conference on Networking, Sensing and Control, ICNSC'06
Y2 - 23 April 2006 through 25 April 2006
ER -