Embedded System Architecture for Real-Time Rule-Based Reasoning

We propose an embedded architecture aimed for the applications of rule-based reasoning systems with real-time restrictions. Such systems are utilized mainly for monitoring, on-line diagnostics and control applications, particularly in the domains of process control, automotive industry, medical applications and other critical systems. The proposed architecture utilizes programmable hardware for acceleration of the actual reasoning process, thus unloading the main processor from the task and releasing its resources for other critical tasks which are bound with the real-time system control. The experimental results show that the architecture is suitable for aimed application area and capable of holding rule-based systems of appropriate sizes.

[1]  Jian Ding,et al.  A FAULT DIAGNOSIS AND OPERATION ADVISING COOPERATIVE EXPERT SYSTEM BASED ON MULTI-AGENT TECHNOLOGY , 2006 .

[2]  Nomusa Dlodlo,et al.  A DECISION SUPPORT SYSTEM FOR WOOL CLASSIFICATION , 2009 .

[3]  Li Lin,et al.  LUBRES: An expert system development and implementation for real-time fault diagnosis of a lubricating oil refining process , 2008, Expert Syst. Appl..

[4]  Alexander B. Sideridis,et al.  GEDAS: an integrated geographical expert database system , 2003, Expert Syst. Appl..

[5]  Manuel Rodríguez,et al.  PLANTWIDE CONTROL DESIGN USING AN EXPERT SYSTEM , 2002 .

[6]  Robert Chun.  Software Integration of Real-Time Expert Systems , 1994 .

[7]  Hidetoshi Onodera,et al.  Hardware architecture for Kohonen network , 1990, IEEE International Symposium on Circuits and Systems.

[8]  Pierre Morizet-Mahoudeaux.  On-Board and Real-Time Expert Control , 1996, IEEE Expert.

[9]  Miguel Rodríguez Gómez,et al.  Expert System Hardware for Fault Detection , 1998, Applied Intelligence.

[10]  T. Martin McGinnity,et al.  Self-repair of embedded systems , 2004, Eng. Appl. Artif. Intell..

[11]  Amr El Abbadi,et al.  Hardware Acceleration of Database Operations Using Content-Addressable Memories. , 2005 .

[12]  Thomas J. Laffey,et al.  Real-Time Knowledge-Based Systems , 1988, AI Mag..

[13]  Vice President,et al.  An Introduction to Expert Systems , 1989 .

[14]  Capers Jones,et al.  Embedded Software: Facts, Figures, and Future , 2009, Computer.

[15]  Chin-Fu Kuo,et al.  Energy-Efficient Scheduling for Real-Time Systems on Dynamic Voltage Scaling (DVS) Platforms , 2007, 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2007).

[16]  Yusen Chen,et al.  DEVELOPING A LARGE-SCALE URBAN DECISION SUPPORT SYSTEM , 2006 .

[17]  Valentin Georgiev Stanchev.  CONSULTING EXPERT SYSTEM FOR CORELESS INDUCTION FURNACES CONTROL , 2006 .

[18]  Charles L. Forgy,et al.  Rete: a fast algorithm for the many pattern/many object pattern match problem , 1991 .

[19]  Chrissanthi Angeli,et al.  Online expert systems for fault diagnosis in technical processes , 2008, Expert Syst. J. Knowl. Eng..

[20]  Andreas Wichert.  Associative diagnosis , 2005, Expert Syst. J. Knowl. Eng..

[21]  Zdenko Kovacic,et al.  An expert system for freshwater fish-farming industry , 2002 .

[22]  Jacek Mazurkiewicz.  Systolic Realization of Kohonen Neural Network , 2005, ICANN.

[23]  Joseph Y.-T. Leung,et al.  Handbook of Real-Time and Embedded Systems , 2007 .