Rough set theory for intelligent industrial applications

Application of intelligent methods in industry has become a very challenging issue nowadays and will be of extreme importance in the future. Intelligent methods include fuzzy sets, neural networks, genetic algorithms and other techniques known as soft computing. No doubt, rough set theory can also contribute to this domain. In the paper basic ideas of rough set theory are presented and some possible intelligent industrial applications outlined.

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

[2]  W. Ziarko,et al.  Rough sets applied to materials data , 1996 .

[3]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[4]  Roman Slowinski,et al.  The Rough Sets Approach to Knowledge Analysis for Classification Support in Technical Diagnostics of Mechanical Objects , 1992, IEA/AIE.

[5]  Shusaku Tsumoto,et al.  Modelling Medical Diagnostic Rules Based on Rough Sets , 1998, Rough Sets and Current Trends in Computing.

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

[7]  J. Kacprzyk,et al.  Incomplete Information: Rough Set Analysis , 1997 .

[8]  C. J. V. Rijsbergen,et al.  Rough Sets, Fuzzy Sets and Knowledge Discovery , 1994, Workshops in Computing.

[9]  Wojciech Ziarko,et al.  Control Algorithm Acquisition, Analysis and Reduction: A Machine Learning Approach , 1989 .

[10]  Zdzislaw Pawlak,et al.  Reasoning about Data - A Rough Set Perspective , 1998, Rough Sets and Current Trends in Computing.

[11]  Tsau Young Lin,et al.  Fuzzy Controllers: An Integrated Approach Based on Fuzzy Logic, Rough Sets, and Evolutionary Computing , 1997 .

[12]  Roman Słowiński,et al.  Intelligent Decision Support , 1992, Theory and Decision Library.

[13]  Lech Polkowski,et al.  Rough Sets in Knowledge Discovery 2 , 1998 .

[14]  Sadaaki Miyamoto,et al.  Rough Sets and Current Trends in Computing , 2012, Lecture Notes in Computer Science.

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

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

[17]  津本 周作,et al.  The Fourth International Workshop on rough sets, fuzzy sets, and machine discovery : proceedings , 1996 .

[18]  Nick Cercone,et al.  Applying Knowledge Discovery to Predict Water-Supply Consumption , 1997, IEEE Expert.

[19]  P. Lingras Rough Neural Networks , 1996 .

[20]  Lluís A. Belanche Muñoz,et al.  A Knowledge-based System for the Diagnosis of Waste-Water Treatment Plants , 1992, IEA/AIE.

[21]  Roman Słowiński,et al.  Evaluation of vibroacoustic diagnostic symptoms by means of the rough sets theory , 1992 .

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

[23]  Zdzislaw Pawlak,et al.  Rough Set Theory and its Applications to Data Analysis , 1998, Cybern. Syst..

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

[25]  Toshinori Munakata,et al.  Rough Control: A Perspective , 1997 .

[26]  Wojciech Ziarko,et al.  Rough sets approach to system modelling and control algorithm acquisition , 1993, IEEE WESCANEX 93 Communications, Computers and Power in the Modern Environment - Conference Proceedings.

[27]  Yiyu Yao,et al.  Rough Sets and Current Trends in Computing , 2001, Lecture Notes in Computer Science.

[28]  R. Słowiński,et al.  Rough sets analysis of diagnostic capacity of vibroacoustic symptoms , 1992 .

[29]  T. Y. Lin,et al.  Rough Sets and Data Mining , 1997, Springer US.