.png){kind=icon}
Return
Automated multi-objective system identification using grammar-based genetic programming
.png){kind=icon}
OA
AI
DOI：10.1016/j.automatica.2023.111017.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In order to use existing identification tools effectively, a user must make critical choices a priori that ultimately determine the quality of estimated models. Furthermore, while estimated models are typically optimized for a single identification criterion, engineering applications typically impose multiple performance specifications that may contradict each other. In this contribution, we develop a system identification methodology that automatically selects parametric model structures from a wide range of dynamic system models based on measured data. The problem of inferring model structures and estimating model parameters within these structures is encapsulated in a bi-level optimization problem. The optimization problem is formulated for multiple user-specified identification objectives. Finally, the range of dynamical systems considered for the optimization problem is specified using Tree Adjoining Grammar. A solution approach based on genetic programming is developed, and its asymptotic properties and computational complexity is analysed. The empirical performance of the proposed identification techniques is studied using a simulation example. (c) 2023 Published by Elsevier Ltd.
Keywords:
System identification
Tree adjoining grammar
Evolutionary algorithms
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.9
Papers:
1.2W
Citations:
5.2W
Organization
Cited Papers
Cognitive processes of the brain: An ultrametric model of information dynamics in unconsciousness
.png){kind=icon}
PREAI
ShareSaveMultiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach
PREAI
ShareSave
ShareSave
View more