A Unified High-Level Petri Net Formalism for Time-Critical Systems
IEEE Transactions on Software Engineering
Design optimization with uncertain application knowledge
IEA/AIE'1997 Proceedings of the 10th international conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems
Functional representation of designs and redesign problem solving
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Combined qualitative-quantitative steady-state diagnosis of continuous-valued systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
| Hi-index | 0.00 |
Traditional methods for parametric design optimization assume that the relations between performance criteria and design variables are known algebraic functions with fixed coefficients. However, the relations may be mutable, i.e., the functions and/or coefficients may not be known explicitly because they depend on input parameters and vary in different parts of the design space. We present a model-based reasoning methodology to support parametric, mutable, design optimization. First, we derive event models to represent the effects of the system's parameters on the material that flows through it. Next, we use these models to discover mutable relations between the system's design variables and its optimization criteria. We then present an algorithm that searches for "optimal" designs by employing sensitivity analysis techniques on the derived relations.