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Tytuł artykułu

A novel adaptation approach for electromagnetic device optimization

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Języki publikacji
EN
Abstrakty
EN
The ability of case-based reasoning systems to solve new problems mainly depends on their case adaptation knowledge and adaptation strategies. In order to carry out a successful case adaptation in our case-based reasoning system for a low frequency electromagnetic device design, we make use of semantic networks to organize related domain knowledge, and then construct a rule-based inference system which is based on the network. Furthermore, based on the inference system, a novel adaptation algorithm is proposed to derive a new device case from a real-world induction motor case-base with high dimensionality.
Rocznik
Strony
473--483
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
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autor
Bibliografia
  • [1] Kolodner J., Case-based reasoning. Morgan Kaufmann: San Mateo, CA (1993).
  • [2] Sowa J.F., Semantic nNetworks. [in:] Shapiro S.C. (ed.), Encyclopedia of artificial intelligence. 2nd edition, New York: John Wiley, pp. 1493-1511 (1992).
  • [3] Ouyang J., Lowther D., The use of semantic networks to adapt a design prototype for electromagnetic device optimization. 14th Biennial IEEE Conference on EM Field Computation, Chicago, USA (2010).
  • [4] Bonzano A., ISAC: A case-based reasoning system for aircraft conflict resolution. Ph.D. Thesis, University of Dublin (1998).
  • [5] Haddad M., Moertl D., Porenta G., SCINA: A case-based reasoning system for the interpretation of myocardial perfusion scintigrams. Proc Computers in Cardiology 761-764 (1995).
  • [6] Grimnes M., Aamodt A., A two layer case-based reasoning architecture for medical image understanding. Advances in Case-Based Reasoning 164-178 (1996).
  • [7] Doyle M., Web-based CBR in Java, final year project. Ph. D. thesis, Trinity College Dublin, (1997).
  • [8] Malek M., Rialle V., Design of a case-based reasoning system applied to neuropathy diagnosis. Advances in Case-Based Reasoning 255-265 (1995).
  • [9] Ashley K., McLaren B., Reasoning with reasons in case-based comparisons. Case-Based Reasoning Research and Development 133-144 (1995).
  • [10] Hastings J., Global and case-specific model-based adaptation. Proc AAAI Fall Symposium on Adaptation of knowledge for Reuse (1995).
  • [11] Arcos J., de Mantaras R., Serra X., Saxex: a case-based reasoning system for generating expressive musical performances. Journal of New Music Research 27: 194-210 (1998).
  • [12] Lieber J., Napoli A., Adaptation of synthesis plans in organic chemistry. Proc Workshop Adaptation in Case-Based reasoning (ECAI) (1996).
  • [13] Leake D., Kinley A., Wilson D., Acquiring case adaptation knowledge: a hybrid approach. AAAI Proceedings 684-689 (1996).
  • [14] Pal S.K., Shiu S.C.K., Foundations of soft case-based reasoning. Morgan Kaufmann: Wiley, (2004).
  • [15] Jess 7.0p1, The rule Engine for the Java™ Platform. Ernest Friedman-Hill, Sandia National Laboratories, CA (2008).
  • [16] Lowther D., ECSE 549: expert systems in electrical design. Lecture Notes, McGill University, (2011).
  • [17] Hubert C.I., Electric machines: theory, operation, applications, adjustment, and controls. Merrill, Columbus, OH. (1991).
  • [18] Bouffard F., ECSE 361: power engineering. Lecture Notes, McGill University (2011).
  • [19] Marler R.T., Arora J.S., Survey of multi-objective optimization methods for engineering. Structural and multidisciplinary optimization 26: 369-395 (2004).
  • [20] MagNet Users manual. Infolytica Corporation, Montreal, Canada (2011).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPS2-0063-0051
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