A qualitative physics based on confluences
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Commonsense reasoning about causality: deriving behavior from structure
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
The bounding approach to VLSI circuit simulation
The bounding approach to VLSI circuit simulation
Artificial Intelligence
An interval method for systems of ODE
Proceedings of the International Symposium on interval mathematics on Interval mathematics 1985
Coordinating the use of qualitative and quantitative knowledge in declarative device modeling
Artificial intelligence, simulation & modeling
Robust multivariable feedback control
Robust multivariable feedback control
Qualitative-numeric simulation with Q3
Recent advances in qualitative physics
Feedback Design of Systems with Significant Uncertainty
Feedback Design of Systems with Significant Uncertainty
Methods and Applications of Interval Analysis (SIAM Studies in Applied and Numerical Mathematics) (Siam Studies in Applied Mathematics, 2.)
Model-based monitoring of dynamic systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Distributed synchronization under uncertainty: A fuzzy approach
Fuzzy Sets and Systems
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Semiquantitative models combine both qualitative and quantitative knowledge within a single semiquantitative qualitative differential equation (SQDE) representation. With current simulation methods, the quantitative knowledge is not exploited as fully as possible. This paper describes dynamic envelopes - a method to exploit quantitative knowledge more fully by deriving and numerically simulating an extremal system whose solution is guaranteed to bound all solutions of the SQDE. It is shown that such systems can be determined automatically given the SQDE and an initial condition. As model precision increases, the dynamic envelope bounds become more precise than those derived by other semiquantitative inference methods. We demonstrate the utility of our method by showing how it improves the dynamic monitoring and diagnosis of a vacuum pump down system.