Automated multi-objective system identification using grammar-based genetic programming

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@Article{KHANDELWAL:2023:automatica,

• author = "Dhruv Khandelwal and Maarten Schoukens and Roland Toth",
• title = "Automated multi-objective system identification using grammar-based genetic programming",
• journal = "Automatica",
• volume = "154",
• pages = "111017",
• year = "2023",
• ISSN = "0005-1098",
• DOI = "doi:10.1016/j.automatica.2023.111017",
• URL = "https://www.sciencedirect.com/science/article/pii/S0005109823001723",
• keywords = "genetic algorithms, genetic programming, System identification, Tree adjoining grammar, Evolutionary algorithms",
• abstract = "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",

}

Genetic Programming entries for Dhruv Khandelwal Maarten Schoukens Roland Toth

Citations