Specifications
Classification code: 745.1.1 Printing Equipment - 745.1 Printing - 723.4 Artificial
Intelligence - 903 Information Science - 715 Electronic Equipment, General Purpose and
Industrial - 704 Electric Components and Equipment - 601.2 Machine Components - 705.3
Electric Motors
DOI: 10.1007/978-3-642-18134-4_84
Database: Compendex
Compilation and indexing terms, © 2013 Elsevier Inc.
6.
Accession number: 20133116556862
Title: An approach for system model identification
Authors: Xu, Xiaoping1 ; Qian, Fucai1 ; Wang, Feng2/徐小平;钱富才;王峰
Author affiliation:
1 School of Sciences, Xi'an University of Technology, Xi'an, China
2 State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute,
Xi'an Jiaotong University, Xi'an, China
Source title: Lecture Notes in Electrical Engineering
Abbreviated source title: Lect. Notes Electr. Eng.
Volume: 125 LNEE
Issue: VOL. 2
Monograph title: Recent Advances in Computer Science and Information Engineering
Issue date: 2012
Publication year: 2012
Pages: 99-104
Language: English
ISSN: 18761100
E-ISSN: 18761119
Document type: Conference article (CA)
Conference name: 2009 11th IEEE International Conference on e-Health Networking,
Applications and Services, Healthcom 2009
Conference date: December 16, 2009 - December 18, 2009
Conference location: Sydney, Australia
Conference code: 79478
Publisher: Springer Verlag, Tiergartenstrasse 17, Heidelberg, D-69121, Germany
Abstract: A method is investigated for system model identification in this paper. The idea of
the scheme employs a system model composed with classical models so as to transform the
system structure identification into a combinational problem. The bacterial foraging optimization
technique is then applied to implement the identification on the structure and parameters.
Finally, simulation results indicate the rationality of the proposed method. © 2012
Springer-Verlag GmbH.
Number of references: 6
Main heading: Electrical engineering
Controlled terms: Mathematical techniques
Uncontrolled terms: Bacterial foraging optimization - Classical
model - Combinational problems - System model identification - System