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Metal model based fuzzy Petri nets back propagation learning algorithm

  • Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In fuzzy production rule-based system, fuzzy Petri nets (FPN) is widely used for its advantage of fuzzy knowledge representation and concurrent reasoning. For the reason that back propagation (BP) algorithm can not be applied to learning of FPN directly without add virtual nodes, To overcome the drawback, a metal fuzzy Petri nets (MFPN) model is proposed. FPN mapped from four elementary production rules can be uniformed by MFPN. A continuous function maps from certainty factor of antecedent propositions to that of consequent ones in MFPN is defined, based on which, a forward continues reasoning algorithm is presented, then the gradient function of certainty factor of consequent propositions with respect to input arc weight is given. To improve convergence speed, Levenberg-Marquardt method is adopted to arc weight optimization.

Original languageEnglish
Title of host publicationIMACS Multiconference on "Computational Engineering in Systems Applications", CESA
Pages1853-1857
Number of pages5
DOIs
StatePublished - 2006
EventIMACS Multiconference on "Computational Engineering in Systems Applications", CESA - Beijing, China
Duration: 4 Oct 20066 Oct 2006

Publication series

NameIMACS Multiconference on "Computational Engineering in Systems Applications", CESA

Conference

ConferenceIMACS Multiconference on "Computational Engineering in Systems Applications", CESA
Country/TerritoryChina
CityBeijing
Period4/10/066/10/06

Keywords

  • Back propagation algorithm
  • Fuzzy Petri nets
  • Levenberg-Marquardt algorithm

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