XIMKON—An Expert Simulation and Control Program

Konar, A. F.; Thuraisingham, Bhavani; Felix, P.E.

doi:10.1016/b978-0-12-480575-0.50011-3

Cited by 6 publications

(6 citation statements)

References 17 publications

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“…Undoubtedly QR (qualitative reasoning) at large should embody knowledge about linearity, dynamical systems theory, and numerical analysis. There already exists software, in the form of numerical algorithms written mostly in C or FORTRAN, automating mathematical techniques; and software which acts as an intelligent coordinating interface among numerical modules, as well as between a user and the numerical modules (e.g., Konar et al 1990), is a step further in this direction.What is not automated is the formulation of real scientific and engineering problems in a solvable mathematical form; i.e., the reasoning of the expert scientists or engineers who are users of existing sophisticated mathematical software. Expert reasoning requires skills which are still informal in the experts' minds and need to be addressed by QR.…”

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Some Features of Expert Reasoning in Qualitative Physics

Mavrovouniotis¹

1992

Computational Intelligence

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Sacks and Doyle (1992) point out the absence of mathematics from SPQR (simulation of processes by qualitative reasoning). Undoubtedly QR (qualitative reasoning) at large should embody knowledge about linearity, dynamical systems theory, and numerical analysis. There already exists software, in the form of numerical algorithms written mostly in C or FORTRAN, automating mathematical techniques; and software which acts as an intelligent coordinating interface among numerical modules, as well as between a user and the numerical modules (e.g., Konar et al. 1990), is a step further in this direction.What is not automated is the formulation of real scientific and engineering problems in a solvable mathematical form; i.e., the reasoning of the expert scientists or engineers who are users of existing sophisticated mathematical software. Expert reasoning requires skills which are still informal in the experts' minds and need to be addressed by QR. These are largely domain-and task-specific skills; while some unifying formalisms will undoubtedly emerge, we may be better off looking for them only after we tackle several specific subdomains.Automated construction of models is one of the important issues, and there has been a lot of quiet work in that direction (e.g., Stephanopoulos et al. 1987). Experts formulate, analyze, and revise specific equations (Sacks and Doyle 1992); what QR and A1 should focus on is not the mathematical side of using equations but the model construction and revision process which uses (a) information about the physical system; (b) the structure and results of previously tried models, analyzed and compared; and (c) information about the ultimate applicarion of the model which determines the goals of the modeling effort, because expert assumptions and simplifications are always context dependent.The context dependence of modeling is one of the most important characteristics of expert reasoning. It has not been addressed by SPQR, but it also receives little attention in Sacks and Doyle (1992). The expert makes just the right assumptions, drops what is unimportant or negligible in each particular portion of the model and for the particular system and task at hand, and comes up with a model ofjust the right complexity.The models and values we use in practice are never accurate. Variables never assume an exact value and they never become exactly equal to each other. To use the -, 0, and + values of SPQR, we have to decide how small a value is considered qualitatively zero, and we similarly have to determine when two values are considered equal. Order-ofmagnitude reasoning systems, such as O[M] (Mavrovouniotis and Stephanopoulos 1987, 1988;Mavrovouniotis et al. 1989), FOG (Raiman 1986;Dague et al. 1987), and other systems (Dubois and Prade 1989), aimed to address, in part, the issue of distinguishing between negligible and important parameters. These systems focused on relationships between parameters (which is a better approach than direct reference to "small" and "large" values), but they did not addre...

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Some Features of Expert Reasoning in Qualitative Physics

Mavrovouniotis¹

1992

Computational Intelligence

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“…The CSTR model was taken !km [28]: see also [29]. The relevant equations are given below: The first of these is the reactor component continuity equation, the second the reactor energy equation.…”

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Trajectory tracking with dynamic neural networks

Konar

Becerikli

Samad

Proceedings of 12th IEEE International Symposium on Intelligent Control

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The application of artificial neural networks to dynarmcal systems has been constrained by the nondynamical nature popular network architectures. Many of the difficulties that ensue-large network sizes, long training times, the need to predetermine buffer lengthscan be overmmed with dynamic neural networks. The minimization of a quadratic performance index is considered for trajectory tracking or process simulation applications. Two approaches for gradient computation are discussed: forward and adjoint sensitivity analysis. The computational complexity of the latter is significantly less, but it requires a backward integration capability We also dlscuss two parameter updating methods: gradlent descent and a Levenberg-Marquardt approach.

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“…Some attempts have been made to design secure object-oriented database systems also [THURB~S] which are being increasingly used for many applications such as military. CAD/CAM, and Process Control [KONA89). As in the case of relational systems.…”

Section: Introductionmentioning

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Secure query processing in intelligent database management systems

Thuraisingham

[1989 Proceedings] Fifth Annual Computer Security Applications Conference

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In a multilevel secure database management system, users cleared at different security levels access and share a database with data at different sensitivity levels. A serious threat to database security, not adequately addressed at present. is the inference problem. That is, users acquire unauthaizcd information from the responses that they legitimately receive. It is rhe inferemzing capability and deductive poww of intelligent database systems th8t could provide viable approaches for handling the inference problem. In this paper is described secure query pmcessing in intelligent databnse systems. The types of databw systems that are considered a x augmented relational systems. fuzzy relational systems and object-oriented systems.

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XIMKON—An Expert Simulation and Control Program

Cited by 6 publications

References 17 publications

Some Features of Expert Reasoning in Qualitative Physics

Some Features of Expert Reasoning in Qualitative Physics

Trajectory tracking with dynamic neural networks

Secure query processing in intelligent database management systems

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