Metal Model Based Fuzzy Petri Nets Back Propagation Learning Algorithm

Author

Tang, Xin Min ; Zhong, Shi Sheng

Author_Institution

Harbin Inst. of Technol., Harbin

Volume

2

fYear

2006

fDate

4-6 Oct. 2006

Firstpage

1853

Lastpage

1857

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.

Keywords

Petri nets; backpropagation; fuzzy reasoning; fuzzy set theory; gradient methods; knowledge based systems; knowledge representation; arc weight optimization; backpropagation learning algorithm; certainty factor; concurrent reasoning; continuous function maps; fuzzy knowledge representation; fuzzy production rule-based system; gradient function; metal fuzzy Petri nets; Artificial neural networks; Convergence; Fuzzy logic; Fuzzy reasoning; Fuzzy systems; Knowledge representation; Optimization methods; Petri nets; Production systems; Systems engineering and theory; Levenberg-Marquardt algorithm; back propagation algorithm; fuzzy Petri nets;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Engineering in Systems Applications, IMACS Multiconference on

Conference_Location

Beijing

Print_ISBN

7-302-13922-9

Electronic_ISBN

7-900718-14-1

Type

conf

DOI

10.1109/CESA.2006.4281940

Filename

4281940