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Authors: Ivan Ryzhikov ; Christina Brester and Eugene Semenkin

Affiliation: Siberian State Aerospace University, Russian Federation

ISBN: 978-989-758-263-9

Keyword(s): Linear Time Invariant Systems, System Identification, Order Reduction, Multi-objective Optimization, Evolution-based Algorithms, Meta-heuristic, Restart Operator.

Related Ontology Subjects/Areas/Topics: Engineering Applications ; Evolutionary Computation and Control ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Optimization Algorithms ; Robotics and Automation ; Signal Processing, Sensors, Systems Modeling and Control ; System Identification ; System Modeling

Abstract: An order reduction problem for linear time invariant models brought to the multi-objective optimization problem is considered. Each criterion is multi-extremum and complex, requires an efficient tool for estimating the parameters of the lower order system and characterizes the model adequacy for the unit-step and Dirac function inputs. A common problem definition is to estimate the lower order model coefficients by minimizing the distance between the output of this model and the initial one. We propose an evolution-based multi-objective stochastic optimization algorithm with a restart operator implemented. The algorithm performance was estimated on two order reduction problems for a single input-single output system and a multiple input-multiple output one. The effectiveness of the algorithm increased sufficiently after implementing a meta-heuristic restart operator. It is shown that the proposed approach is comparable to other approaches, but allows a Pareto-front approximation to be found and not just a single solution. (More)

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Paper citation in several formats:
Ryzhikov, I.; Brester, C. and Semenkin, E. (2017). Multi-objective Order Reduction Problem Solving with Restart Meta-heuristic Implementation.In Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-263-9, pages 270-278. DOI: 10.5220/0006431002700278

@conference{icinco17,
author={Ivan Ryzhikov. and Christina Brester. and Eugene Semenkin.},
title={Multi-objective Order Reduction Problem Solving with Restart Meta-heuristic Implementation},
booktitle={Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2017},
pages={270-278},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006431002700278},
isbn={978-989-758-263-9},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Multi-objective Order Reduction Problem Solving with Restart Meta-heuristic Implementation
SN - 978-989-758-263-9
AU - Ryzhikov, I.
AU - Brester, C.
AU - Semenkin, E.
PY - 2017
SP - 270
EP - 278
DO - 10.5220/0006431002700278

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