A model accuracy and validation algorithm
Ημερομηνία
2002ISBN
0-7918-3629-0978-0-7918-3629-3
Εκδότης
American Society of Mechanical Engineers (ASME)Source
ASME International Mechanical Engineering Congress and Exposition, ProceedingsPages
573-583Google Scholar check
Keyword(s):
Metadata
Εμφάνιση πλήρους εγγραφήςΕπιτομή
Dynamic models of physical systems with physically meaningful states and parameters have become increasingly important, for design, control and even procurement decisions. The successful use of models in these contexts requires that the models be of sufficient quality. However, while algorithms have been developed to help formulate and integrate physical system models, as well as to generate minimum complexity physical system models, algorithms to assess the "quality" of dynamic system models have not been produced. This is true even if the attributes of model are limited to accuracy and validity. The objective of this paper is to introduce a new methodology that systematically quantifies the accuracy of a predicted system response and determines the validity of the physical system model used to predict the system response. The accuracy and validity of the model are evaluated using statistical properties of measured system response. The new algorithm is called Accuracy & Validation Algorithm for Simulation (AVASIM), and is a time-domain perspective comparing the model's time trajectories at user-defined points of interest as well as over the entire simulation horizon. To illustrate AVASIM, the quality of a handling model of a DaimlerChrysler Grand Cherokee is compared to the measurements obtained from that vehicle subjected to known steering inputs. Results demonstrate that the accuracy and validity of the Grand Cherokee model can be systematically assessed using the proposed methodology, and, thus, AVASIM appears to be a powerful tool for assessing the quality of system models. Copyright © 2002 by ASME.
Collections
Cite as
Related items
Showing items related by title, author, creator and subject.
-
Article
Energy-based model reduction methodology for automated modeling
Louca, Loucas S.; Stein, J. L.; Hulbert, G. M. (2010)In recent years, algorithms have been developed to help automate the production of dynamic system models. Part of this effort has been the development of algorithms that use modeling metrics for generating minimum complexity ...
-
Article
A review of proper modeling techniques
Ersal, T.; Fathy, H. K.; Rideout, D. G.; Louca, Loucas S.; Stein, J. L. (2008)A dynamic system model is proper for a particular application if it achieves the accuracy required by the application with minimal complexity. Because model complexity often-but not always-correlates inversely with simulation ...
-
Conference Object
Computational modeling of visual selective attention
Neokleous,Kleanthis C.; Schizas, Christos N. (2011)An overview of a nerocomputational model of visual selective attention that has been properly implemented is presented in this abstract. Visual selective attention is a fundamental function of human cognition and a highly ...