An Application of Rough Set Methods in Control Design

The paper deals with an automatic concurrent control design method derived from the specification of a discrete event control system represented in the form of a decision table. The main stages of our approach are: the control specification by decision tables, generation of rules from the specification of the system behavior, and converting rules set into a concurrent program represented in the form of a Petri net. Our approach is based on rough set theory [17].

[1]  Zbigniew Suraj,et al.  Reconstruction of Cooperative Information Systems under Cost Constraints: A Rough Set Approach , 1998, Inf. Sci..

[2]  Adam Mrózek,et al.  Rough Sets in Computer Implementation of Rule-Based Control of Industrial Processes , 1992, Intelligent Decision Support.

[3]  Andrzej Skowron,et al.  The Discernibility Matrices and Functions in Information Systems , 1992, Intelligent Decision Support.

[4]  Zdzisław Pawlak,et al.  ROUGH CONTROL APPLICATION OF ROUGH SET THEORY TO CONTROL , 1996 .

[5]  Andrzej Skowron,et al.  Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems , 1998 .

[6]  A. Mrozek,et al.  The methodology of rough controller synthesis , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[7]  Zdzisław Pawlak,et al.  Decision tables and decision algorithms , 1985 .

[8]  Ingo Wegener,et al.  The complexity of Boolean functions , 1987 .

[9]  Stuart C. Shapiro,et al.  Encyclopedia of artificial intelligence, vols. 1 and 2 (2nd ed.) , 1992 .

[10]  Morton Nadler,et al.  Pattern recognition engineering , 1993 .

[11]  R. Słowiński.  Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory , 1992 .

[12]  James F. Peters,et al.  Approximate Time Rough Control: Concepts and Application to Satellite Attitude Control , 1998, Rough Sets and Current Trends in Computing.

[13]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[14]  Zdzisław Pawlak,et al.  The idea of a rough fuzzy controller and its application to the stabilization of a pendulum-car system , 1995 .

[15]  Andrzej Skowron,et al.  Rough-Fuzzy Hybridization: A New Trend in Decision Making , 1999 .

[16]  Andrzej Skowron,et al.  EXTRACTING LAWS FROM DECISION TABLES: A ROUGH SET APPROACH , 1995, Comput. Intell..

[17]  MengChu Zhou,et al.  Petri net synthesis for discrete event control of manufacturing systems , 1992, The Kluwer international series in engineering and computer science.

[18]  Witold Pedrycz,et al.  Approximate real‐time decision making: Concepts and rough fuzzy Petri net models , 1999 .

[19]  Wojciech Ziarko.  Acquisition of Control Algorithms from Operation Data , 1992, Intelligent Decision Support.

[20]  Toshinori Munakata,et al.  Fundamentals of the new artificial intelligence - beyond traditional paradigms , 2001, Graduate texts in computer science.

[21]  Zbigniew Suraj,et al.  Rough set methods for the synthesis and analysis of concurrent processes , 2000 .

[22]  Tsau Young Lin,et al.  Rough Sets and Data Mining: Analysis of Imprecise Data , 1996 .

[23]  Hans-Michael Hanisch,et al.  A Signal Extension for Petri Nets and its Use in Controller Design , 2000, Fundam. Informaticae.