Substructure preservation based approach for discrete time system approximation

Substructure preservation based approach for discrete time system approximation In this study, a new technique for discrete time system reduction is suggested which preserves the substructure of the higher order system in the reduced system. Motivated by various system reduction and optimization techniques available in the literature, the proposed technique is based on Cuckoo search which is used to obtain unknown elements of the reduced system with an error criterion minimization. The efficacy of the proposed technique is justified by reducing few benchmark systems and the obtained results are compared with other well-known order reduction methods existing in the literature. 1 Introduction bilinear, linear transformation, etc. (Chu and Glover 1999; Shih and WD 1973). Finally the reduced order model is Reduced order modelling has become a significant area of achieved using corresponding inverse transformation. In research in systems engineering since 1960s. Numerous the recent years, artificial neural network (ANN) (Alsmadi techniques are developed for continuous and discrete time et al. 2011), genetic algorithm (GA) (Alsmadi and Abo- systems (Moore 1981; Aoki 1968; Shamash 1974; Hutton Hammour 2015), step response matching (SRM) and Friedland 1975; Obinata and Inooka 1983; El-Attar (Mukherjee et al. 2007) and differential evolution (DE) and Vidyasagar 1978; Bistritz 1982; Hwang et al. 1983; (Namratha and http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Microsystem Technologies Springer Journals

Substructure preservation based approach for discrete time system approximation

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Engineering; Electronics and Microelectronics, Instrumentation; Nanotechnology; Mechanical Engineering
ISSN
0946-7076
eISSN
1432-1858
D.O.I.
10.1007/s00542-018-3985-0
Publisher site
See Article on Publisher Site

Abstract

In this study, a new technique for discrete time system reduction is suggested which preserves the substructure of the higher order system in the reduced system. Motivated by various system reduction and optimization techniques available in the literature, the proposed technique is based on Cuckoo search which is used to obtain unknown elements of the reduced system with an error criterion minimization. The efficacy of the proposed technique is justified by reducing few benchmark systems and the obtained results are compared with other well-known order reduction methods existing in the literature. 1 Introduction bilinear, linear transformation, etc. (Chu and Glover 1999; Shih and WD 1973). Finally the reduced order model is Reduced order modelling has become a significant area of achieved using corresponding inverse transformation. In research in systems engineering since 1960s. Numerous the recent years, artificial neural network (ANN) (Alsmadi techniques are developed for continuous and discrete time et al. 2011), genetic algorithm (GA) (Alsmadi and Abo- systems (Moore 1981; Aoki 1968; Shamash 1974; Hutton Hammour 2015), step response matching (SRM) and Friedland 1975; Obinata and Inooka 1983; El-Attar (Mukherjee et al. 2007) and differential evolution (DE) and Vidyasagar 1978; Bistritz 1982; Hwang et al. 1983; (Namratha and

Journal

Microsystem TechnologiesSpringer Journals

Published: Jun 4, 2018

References

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