Online modeling refinement for discrete event systems

Author

Chung, Sheng-Luen ; Li, Chung-Lnn ; Wu, Jun-Chin ; Wang, Shih-Tung

Author_Institution

Dept. of Electr. Eng., Nat. Taiwan Univ. of Sci. & Technol., Taipei, Taiwan

Volume

3

fYear

2003

fDate

5-8 Oct. 2003

Firstpage

2739

Abstract

Machine identification of discrete event systems (DES) addresses the issue of identifying an unknown system based on externally observed sample path of the unknown system. Online Modeling Refinement studies the continuing machine identification process in the context when the observed sample path is updated incrementally. While machine identification problem for fixed length sample path is NP-complete, the computational requirement for the proposed online modeling refinement algorithm is maintained at minimal by taking the structure similarity between successive accumulated observed sample paths. In addition to the computational advantage, the proposed algorithm also guarantees the identification results of the system models "converge" to the unknown DEDS model as the incrementally observed sequence get "long" and "rich" enough.

Keywords

computational complexity; discrete event systems; finite automata; refinement calculus; DEDS model; NP-complete; accumulated observed sample path; discrete event systems; externally observed sample path; fixed length sample path; machine identification; online modeling refinement algorithm; Automatic testing; Context modeling; Councils; Discrete event systems; Formal languages; Learning automata; Mathematical model; State estimation; System identification; System testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 2003. IEEE International Conference on

ISSN

1062-922X

Print_ISBN

0-7803-7952-7

Type

conf

DOI

10.1109/ICSMC.2003.1244299

Filename

1244299