Authors:
Ivan Ryzhikov
;
Eugene Semenkin
and
Ilia Panfilov
Affiliation:
Siberian State Aerospace University, Russian Federation
Keyword(s):
Dynamic System, Linear Differential Equation, Evolutionary Strategies, Parameters Identification, Initial Value Estimation, Order Estimation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Industrial Engineering
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
;
System Modeling
;
Systems Modeling and Simulation
Abstract:
A dynamic system identification problem is considered. It is an inverse modelling problem, where one needs to find the model in an analytical form and a dynamic system is represented with the observation data. In this study the identification problem was reduced to an optimization problem, and in such a way every solution of the extremum problem determines a linear differential equation and coordinates of the initial value. The proposed approaches do not require any assumptions of the system order and the initial value coordinates and estimates the model in the form of a linear differential equation. These variables are estimated automatically and simultaneously with differential equation coefficients. Problem-oriented evolution-based optimization techniques were designed and applied. Techniques are based on the evolutionary strategies algorithm and have been improved to achieve efficient solving of the reduced problem for every proposed determination scheme. Experimental results con
firm the reliability of the given approach and the usefulness of the reduced problem solving tool.
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