USING EXPERT SYSTEMS TO TROUBLESHOOT MICROPROCESSOR-BASED CONTROL SYSTEMS: AN EXPECTATION-BASED APPROACH**This work performed at the Center for Automation and Intelligent Systems Research, Case Western Reserve University.

1987 ◽  
pp. 95-98
Author(s):  
P. Schaefer ◽  
H.A. Guvenir
Keyword(s):  
Expert Systems ◽  
Control Systems ◽  
Intelligent Systems ◽  
Systems Research ◽  
Western Reserve

Fundamentals of creating MIVAR expert systems

2021 ◽  
Author(s):  
Oleg Varlamov
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Expert System ◽  
Expert Systems ◽  
Decision Support Systems ◽  
Intelligent Systems ◽  
Autonomous Robots ◽  
Support Systems ◽  
Knowledge Bases ◽  
Computer Engineering

Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI. The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

Cyber Physical Control Systems

2015 ◽  
pp. 263-285
Keyword(s):  
Artificial Intelligence ◽  
Control Systems ◽  
Intelligent Systems ◽  
Control Strategies ◽  
Case Based Reasoning ◽  
Physical Control ◽  
Closed Loop Systems ◽  
Intelligence Models ◽  
Algorithmic Design ◽  
Case Based

The focus of algorithmic design is to solve composite problems. Intelligent systems use intellectual concepts like evolutionary computation, artificial neural networks, fuzzy systems, and swarm intelligence to process natural intelligence models. Artificial intelligence is used as a part of intelligent systems to perform logic- and case-based reasoning. Systems like mechanical and electrical support systems are operated by utilizing Supervisory Control and Data Acquisition (SCADA) systems. These systems cannot accomplish their purpose, provided the control system deals with the reliability of it. In CPSs, dimensions of physical processes are taken by sensors and are processed in cyber subsystems to drive the actuators that affect the physical processors. CPSs are closed-loop systems. The adaptation and the prediction are the properties to be followed by the control strategies that are implemented in cyber subsystems. This chapter explores cyber physical control systems.

Simulation, Design, and Implementation Based Undergraduate Research in Control Systems: Nonlinear Before Linear

2008 ◽  
Author(s):  
Dale B. McDonald ◽  
Idir Azouz ◽  
Carrie-Anne Taylor
Keyword(s):  
Nonlinear Control ◽  
Control Systems ◽  
Research Program ◽  
Undergraduate Research ◽  
Undergraduate Curriculum ◽  
Linear Control Systems ◽  
Simulation Design ◽  
Engineering Research ◽  
Design And Implementation ◽  
Systems Research

A typical undergraduate curriculum introduces linear control systems concepts only, often in a single elective course. This curriculum structure introduces challenges to student involvement in control systems research as nonlinear concepts are the focus of the majority of such efforts. With undergraduate participation in engineering research steadily increasing, nonlinear control concepts must be introduced prior to formal classroom study of linear systems. Given this reality, we propose an intense and relatively brief research program, consisting of three distinct phases. The program objective is to present a targeted educational experience in nonlinear control theory based upon the design and implementation of control laws developed for a particular nonlinear system class. Given significant interaction between the student and the faculty mentor, we believe that an excellent opportunity in undergraduate education and research will be realized, despite the student’s initial unfamiliarity with nonlinear control systems concepts. A research program consisting of three phases is proposed and initial technical results are presented to facilitate a candid discussion of the issues that may prevent undergraduate participation in research and to detail the manner in which many of these obstacles were overcome.

scholarly journals About some possibilities of diagnosing partial failures arising in complex agricultural machinery

2020 ◽  
Vol 175 ◽  
pp. 05004
Author(s):  
Alexy Zotov ◽  
Vadim Gritsenko ◽  
Andrey Gazizov
Keyword(s):  
Decision Support ◽  
Expert Systems ◽  
Decision Support Systems ◽  
Intelligent Systems ◽  
Diagnostic Procedure ◽  
Agricultural Machinery ◽  
Managerial Decision ◽  
Complex Technical Systems ◽  
Multi Level ◽  
Technical Systems

The article is devoted to the issue of partial failure in complex technical systems. The authors analyze the diagnostic procedure in agricultural machinery for various purposes, the results of which provide a diagram of a multi-level organization for assessing the condition of equipment. It is also proposed to expand the range of estimated diagnostic parameters for making a more reliable managerial decision. The introduction of expert systems and other similar intelligent systems (for example, expert decision support systems) for diagnosing complex malfunctions is justified.

Computational Cybernetics

2004 ◽  
Vol 8 (5) ◽  
pp. 453-453
Author(s):  
Wilfried Elmenreich ◽  
◽  
Imre J. Rudas ◽  
Keyword(s):  
Control Systems ◽  
Computational Intelligence ◽  
Intelligent Systems ◽  
Adaptive Systems ◽  
Communication Theory ◽  
Lake Balaton ◽  
Automatic Control Systems ◽  
Processing Information ◽  
Technology Control ◽  
The Comparative Study

This issue contains selected papers from the International IEEE Conference on Computational Cybernetics that took place in August 2003 in Hungary at the site of lake Balaton. Computational Cybernetics is the synergetic integration of Cybernetics and Computational Intelligence techniques. Cybernetics was defined by Wiener as "the science of control and communication, in the animal and the machine". The word "cybernetics" itself stems from the Greek "kybernetes" that means pilot or governor. Thus, the science of computational Cybernetics is especially concerned with the comparative study of automatic control systems. Furthermore, Computational Cybernetics covers not only mechanical, but biological (living), social and economical systems and for this uses computational intelligence based results of communication theory, signal processing, information technology, control theory, the theory of adaptive systems, the theory of complex systems (game theory, operational research), and computer science. We have selected 14 papers from the conference covering the fields of system design and modeling, neural networks, and fuzzy control, which resemble the great variety of computational cybernetics. While it is sometimes difficult to integrate over these differing fields, we expect the evolution of future intelligent systems at the service of mankind by the synergetic integration of these different areas. It is our hope that the papers in this issue will inspire and help our readers in the development of advanced intelligent systems.

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