A memory fuzzy learning for uncertainty management in disassembly

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 2006 IEEE International Conference on Networking, Sensing and Control, ICNSC'06
Pages522-527
Number of pages6
StatePublished - Dec 1 2006
Event2006 IEEE International Conference on Networking, Sensing and Control, ICNSC'06 - Ft. Lauderdale, FL, United States
Duration: Apr 23 2006Apr 25 2006

Publication series

NameProceedings of the 2006 IEEE International Conference on Networking, Sensing and Control, ICNSC'06

Other

Other2006 IEEE International Conference on Networking, Sensing and Control, ICNSC'06
CountryUnited States
CityFt. Lauderdale, FL
Period4/23/064/25/06

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Control and Systems Engineering

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