The Search is performed on all of the following fields:
Note: Please use complete words only.

Publication Title

Abstract

Publication Keywords

DOI

Proceeding Title

Proceeding Foreword

ISBN (Completed)

Insticc Ontology

Author Affiliation

Author Name

Editor Name

If you're looking for an exact phrase use quotation marks on text fields.

Paper

Multi-objective Evolutionary Approach in the Linear Dynamical System Inverse ModelingTopics: Applications: Games and Entertainment Technologies, Evolutionary Robotics, Evolutionary Art and Design, Industrial and Real World applications, Computational Economics and Finance; Co-Evolution and Collective Behavior; Evolutionary Multi-objective Optimization; Evolutionary Search and Meta-heuristics

Keyword(s):Time-invariant System, Dynamical System, Linear Differential Equation, Inverse Problem, Multi-objective Optimization, System Identification, Initial Value.

Related
Ontology
Subjects/Areas/Topics:Artificial Intelligence
;
Co-Evolution and Collective Behavior
;
Computational Intelligence
;
Evolutionary Computing
;
Soft Computing

Abstract: In this study, we consider an inverse mathematical modeling problem for dynamical systems with a single output. Generally, the final solution of this problem is an approximation of a system transient process and a system state at some time point. Only those classes of models, which describe the transient process properly, can portray the system behavior and can be applicable for prediction and optimal control problems. One of possible mathematical representations of dynamical systems is differential equations, in particular, linear differential equations for linear systems. While solving the inverse problem, we aim to identify a differential equation order and parameters, an initial system state. Since all the parameters are interrelated, we propose to identify them by solving a two-criterion optimization problem, which includes the model adequacy (i.e. a distance between model outputs and observations) and the closeness of the initial value estimation to the observation data. To solve this complex optimization problem, we apply a Real-valued Cooperative Multi-Objective Evolutionary Algorithm which effectiveness has been proved on the set of high-dimensional test problems. We investigate the dependency between the considered criteria by depicting the Pareto front approximation. Then, having the same amount of computational resources, we vary the system order, the number of control inputs and the initial state to analyze changes in the algorithm effectiveness based on each criterion and estimate basic limitations. Finally, we conclude that the optimization problem considered is quite challenging and it might be used for testing and comparing various heuristics.(More)

In this study, we consider an inverse mathematical modeling problem for dynamical systems with a single output. Generally, the final solution of this problem is an approximation of a system transient process and a system state at some time point. Only those classes of models, which describe the transient process properly, can portray the system behavior and can be applicable for prediction and optimal control problems. One of possible mathematical representations of dynamical systems is differential equations, in particular, linear differential equations for linear systems. While solving the inverse problem, we aim to identify a differential equation order and parameters, an initial system state. Since all the parameters are interrelated, we propose to identify them by solving a two-criterion optimization problem, which includes the model adequacy (i.e. a distance between model outputs and observations) and the closeness of the initial value estimation to the observation data. To solve this complex optimization problem, we apply a Real-valued Cooperative Multi-Objective Evolutionary Algorithm which effectiveness has been proved on the set of high-dimensional test problems. We investigate the dependency between the considered criteria by depicting the Pareto front approximation. Then, having the same amount of computational resources, we vary the system order, the number of control inputs and the initial state to analyze changes in the algorithm effectiveness based on each criterion and estimate basic limitations. Finally, we conclude that the optimization problem considered is quite challenging and it might be used for testing and comparing various heuristics.

Guests can use SciTePress Digital Library without having a SciTePress account. However, guests have limited access to downloading full text versions of papers and no access to special options.

Guests can use SciTePress Digital Library without having a SciTePress account. However, guests have limited access to downloading full text versions of papers and no access to special options.

Ryzhikov, I.; Brester, C.; Semenkin, E. and Kolehmainen, M. (2018). Multi-objective Evolutionary Approach in the Linear Dynamical System Inverse Modeling.In Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-327-8, pages 281-288. DOI: 10.5220/0007228402810288

@conference{ijcci18, author={Ivan Ryzhikov. and Christina Brester. and Eugene Semenkin. and Mikko Kolehmainen.}, title={Multi-objective Evolutionary Approach in the Linear Dynamical System Inverse Modeling}, booktitle={Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,}, year={2018}, pages={281-288}, publisher={SciTePress}, organization={INSTICC}, doi={10.5220/0007228402810288}, isbn={978-989-758-327-8}, }

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, TI - Multi-objective Evolutionary Approach in the Linear Dynamical System Inverse Modeling SN - 978-989-758-327-8 AU - Ryzhikov, I. AU - Brester, C. AU - Semenkin, E. AU - Kolehmainen, M. PY - 2018 SP - 281 EP - 288 DO - 10.5220/0007228402810288