國立虎尾科技大學 |

Studies into Computational Intelligence Approaches for the Identification of Complex Nonlinear Systems.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Studies into Computational Intelligence Approaches for the Identification of Complex Nonlinear Systems./
Author:	Bolourchi Yazdi, Seyed Ali.
Description:	1 online resource (199 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 75-11(E), Section: B.
Subject:	Computer engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9781321036527

Studies into Computational Intelligence Approaches for the Identification of Complex Nonlinear Systems.
Bolourchi Yazdi, Seyed Ali.

Studies into Computational Intelligence Approaches for the Identification of Complex Nonlinear Systems. - 1 online resource (199 pages)

Source: Dissertation Abstracts International, Volume: 75-11(E), Section: B.

Thesis (Ph.D.)--University of Southern California, 2014.

Includes bibliographical references

This study builds on major advances in the field of Computational Intelligence to develop a state-of-the-art data-driven methodology that provides parsimonious optimized computational models in the form of systems of differential equations that characterize the behavior of complex nonlinear phenomena observed in mechanical and biological systems. The proposed hybrid identification scheme integrates various stochastic optimization methods and computer algebra techniques, such as Genetic Programming and Genetic Algorithms, to evolve structures of differential equations, to optimize their parameters, and to reduce their complexity for controlling bloat. The investigated scenarios include systems that exhibit polynomial-type nonlinearities in their response, systems that show discontinuity in their nonlinear behavior, systems with memory-dependent and dissipative characteristics, as well as the human spine. The investigations are conducted by processing input and output data obtained from synthetic simulations as well as experiments. It is shown that the proposed technique yields reduced-order, reduced-complexity, optimized differential equations, that accurately characterize the behavior of the investigated systems, and provide accurate estimates. The generalization extent of the discovered models is scrutinized by assessing their performance in new dynamical environments through applying validation excitations that are substantially different from the excitations employed for training. Findings reveal that the resulting models provide reasonably accurate estimates, even when models are subjected to new stimulations with various intensities. Thus, the proposed approach of this study presents a robust data-driven methodology based on evolutionary computation techniques that provides elegant computational models to represent variety of complex nonlinear systems.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781321036527Subjects--Topical Terms:

569006
Computer engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Studies into Computational Intelligence Approaches for the Identification of Complex Nonlinear Systems.
LDR:03100ntm a2200337K 4500 001 913738
005 20180622095236.5
006 m o u
007 cr mn||||a|a||
008 190606s2014 xx obm 000 0 eng d
020 $a 9781321036527
035 $a (MiAaPQ)AAI3628122
035 $a (MiAaPQ)usc:15086
035 $a AAI3628122
040 $a MiAaPQ $b eng $c MiAaPQ
100 1 $a Bolourchi Yazdi, Seyed Ali. $3 1186695
245 1 0 $a Studies into Computational Intelligence Approaches for the Identification of Complex Nonlinear Systems.
264 0 $c 2014
300 $a 1 online resource (199 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 75-11(E), Section: B.
500 $a Adviser: Sami F. Masri.
502 $a Thesis (Ph.D.)--University of Southern California, 2014.
504 $a Includes bibliographical references
520 $a This study builds on major advances in the field of Computational Intelligence to develop a state-of-the-art data-driven methodology that provides parsimonious optimized computational models in the form of systems of differential equations that characterize the behavior of complex nonlinear phenomena observed in mechanical and biological systems. The proposed hybrid identification scheme integrates various stochastic optimization methods and computer algebra techniques, such as Genetic Programming and Genetic Algorithms, to evolve structures of differential equations, to optimize their parameters, and to reduce their complexity for controlling bloat. The investigated scenarios include systems that exhibit polynomial-type nonlinearities in their response, systems that show discontinuity in their nonlinear behavior, systems with memory-dependent and dissipative characteristics, as well as the human spine. The investigations are conducted by processing input and output data obtained from synthetic simulations as well as experiments. It is shown that the proposed technique yields reduced-order, reduced-complexity, optimized differential equations, that accurately characterize the behavior of the investigated systems, and provide accurate estimates. The generalization extent of the discovered models is scrutinized by assessing their performance in new dynamical environments through applying validation excitations that are substantially different from the excitations employed for training. Findings reveal that the resulting models provide reasonably accurate estimates, even when models are subjected to new stimulations with various intensities. Thus, the proposed approach of this study presents a robust data-driven methodology based on evolutionary computation techniques that provides elegant computational models to represent variety of complex nonlinear systems.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer engineering. $3 569006
650 4 $a Artificial intelligence. $3 559380
650 4 $a Mechanics. $3 527684
655 7 $a Electronic books. $2 local $3 554714
690 $a 0464
690 $a 0800
690 $a 0346
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Southern California. $b Civil Engineering(Structural Engineering). $3 1186696
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3628122 $z click for full text (PQDT)