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Application of Natural-Inspired Paradigms on System Identification: Exploring the Multivariable Linear Time Variant Case

Application of Natural-Inspired Paradigms on System Identification: Exploring the Multivariable Linear Time Variant Case
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Author(s): Mateus Giesbrecht (Unicamp, Brazil) and Celso Pascoli Bottura (Unicamp, Brazil)
Copyright: 2018
Pages: 50
Source title: Incorporating Nature-Inspired Paradigms in Computational Applications
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-5225-5020-4.ch001

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Abstract

In this chapter, the application of nature-inspired paradigms on system identification is discussed. A review of the recent applications of techniques such as genetic algorithms, genetic programming, immuno-inspired algorithms, and particle swarm optimization to the system identification is presented, discussing the application to linear, nonlinear, time invariant, time variant, monovariable, and multivariable cases. Then the application of an immuno-inspired algorithm to solve the linear time variant multivariable system identification problem is detailed with examples and comparisons to other methods. Finally, the future directions of the application of nature-inspired paradigms to the system identification problem are discussed, followed by the chapter conclusions.

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