An Intelligent Numerical Simulation Environment on Component-Based Models: Part I

2003 ◽  
Author(s):  
Weiwen Deng ◽  
Edward Y. L. Gu
Keyword(s):  
Numerical Simulation ◽  
Expert System ◽  
Dynamic System ◽  
Real World ◽  
Object Oriented ◽  
Physical Nature ◽  
Component Model ◽  
Simulation Environment ◽  
System Technology ◽  
Complex Dynamic

This paper mainly discusses numerical simulation on a component-based model, in which a large complex dynamic system is presumably partitioned into and modeled by a number of interconnected components, which often corresponds to the physical nature in the real world. An independent component computation (ICC) method is proposed to deal numerically with component-based models. With ICC method, each component model is numerically solved independently of, and concurrently with others. This method, combined with the object-oriented methodology and expert system technology, leads to an intelligent numerical simulation environment, which is presented in detail in Part II.

An Intelligent Numerical Simulation Environment on Component-Based Models: Part II

2003 ◽  
Author(s):  
Weiwen Deng ◽  
Edward Y. L. Gu
Keyword(s):  
Numerical Simulation ◽  
Expert System ◽  
Natural Extension ◽  
Object Oriented ◽  
Independent Component ◽  
Simulation Environment ◽  
Simulation Technology ◽  
Systematic Method ◽  
Sequential Computation

An object-oriented simulation technology is first investigated, as a natural extension to the independent component computation (ICC) method under sequential computation environment, proposed in Part I. The intelligence, represented by an embedded expert system, is introduced. A systematic method and structure for establishing an intelligent numerical simulation environment is presented. A Windows-based software, Solver for Windows, has been developed to implement and verify the concepts presented in this paper.

Expert systems for management: Research and applications

1984 ◽  
Vol 9 (4) ◽  
pp. 153-162 ◽  
Author(s):  
Robert W. Blanning
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Real World ◽  
Expert Knowledge ◽  
Management Systems ◽  
Management Research ◽  
System Technology ◽  
Real World Data ◽  
Subjective Data ◽  
World Data

Management scientists and systems designers have long recognized that in some cases objective real world data con tained in their models and systems should be supplemented by subjective data elicited from experienced managers. The recent but growing application of expert system technology to mana gement problems is providing a framework for capturing and using expert knowledge in management systems. In this paper we identify fruitful areas for research and application of expert systems in management.

Extending worksheet modelling software with expert system technology

1986 ◽  
Vol 16 (4) ◽  
pp. 107-117 ◽  
Author(s):  
Gerald A. Sullivan ◽  
Kenneth J. Fordyce
Keyword(s):  
Expert System ◽  
System Technology

Student-Teacher: The Learning Machine as an Instructional Device

1988 ◽  
Vol 4 (1) ◽  
pp. 57-70
Author(s):  
Carol Scheftic ◽  
George Darlington Wood
Keyword(s):  
Functional Analysis ◽  
Expert System ◽  
Socratic Method ◽  
Educational Tool ◽  
System Technology ◽  
Solution Strategy ◽  
Analysis Techniques ◽  
Acquisition Device ◽  
Learning Machine ◽  
Further Development

This article presents QUERIOUS, an educational tool based on expert system technology, functional analysis techniques, and Socratic method. Using a knowledge acquisition device originally developed to allow specialists to impart their knowledge to expert systems, our tool asks students questions, leads them through functional analysis of a problem and, in effect, induces them to teach the system their solution strategy. An existing prototype is described, and a proposal for further development is presented.

Application of a Bayesian network to integrated circuit tester diagnosis

1995 ◽  
Vol 9 (1) ◽  
pp. 51-65 ◽  
Author(s):  
Daniel Mittelstadt ◽  
Robert Paasch ◽  
Bruce D’Ambrosio
Keyword(s):  
Expert System ◽  
Real World ◽  
Integrated Circuit ◽  
Procedural Knowledge ◽  
Knowledge Engineering ◽  
Bayesian Belief Network ◽  
Belief Networks ◽  
Diagnostic Problem ◽  
Ic Testing ◽  
Time Limitations

AbstractResearch efforts to implement a Bayesian belief-network-based expert system to solve a real-world diagnostic problem – the diagnosis of integrated circuit (IC) testing machines – are described. The development of several models of the IC tester diagnostic problem in belief networks also is described, the implementation of one of these models using symbolic probabilistic inference (SPI) is outlined, and the difficulties and advantages encountered are discussed. It was observed that modeling with interdependencies in belief networks simplifies the knowledge engineering task for the IC tester diagnosis problem, by avoiding procedural knowledge and focusing on the diagnostic component’s interdependencies. Several general model frameworks evolved through knowledge engineering to capture diagnostic expertise that facilitated expanding and modifying the networks. However, model implementation was restricted to a small portion of the modeling, that of contact resistance failures, which were due to time limitations and inefficiencies in the prototype inference software we used. Further research is recommended to refine existing methods, in order to speed evaluation of the models created in this research. With this accomplished, a more complete diagnosis can be achieved.

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