.png){kind=icon}
.png){kind=icon}
Return
Statistical tools for optimal dynamic model building
.png){kind=icon}
PRE
AI
DOI：10.1016/S0098-1354(00)00328-8.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A general, systematic procedure is presented to support the development and statistical verification of dynamic process models. Within this procedure, methods are presented to address several key aspects, such as structural identifiability and distinguishability testing, as well as optimal design of dynamic experiments for both model discrimination and improving parameter precision. A novel optimisation-based approach is introduced for testing of model structural identifiability and distinguishability, involving semi-infinite programming and max-min problems. The design of dynamic experiments is cast as an optimal control problem within a framework that enables the calculation of optimal sampling points, experiment duration, fixed and variable external control profiles, and initial conditions of a dynamic experiment subject to general constraints on inputs and outputs. Within this framework, methods are presented to provide experiment design robustness, accounting for parameter uncertainty. The procedure is demonstrated through a practical biotechnology example. (C) 2000 Elsevier Science Ltd. All rights reserved.
Keywords:
optimal experiment design
dynamic modelling
parameter estimation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
C
IF:
3.9
Papers:
8.1K
Citations:
1.7W
Organization
No organization information available