Neurodynamic optimization approaches to robust pole assignment based on alternative robustness measures

Xinyi Le; Jun Wang

doi:10.1109/IJCNN.2013.6706834

Neurodynamic optimization approaches to robust pole assignment based on alternative robustness measures

Xinyi Le
, Jun Wang

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

4 Citations (Scopus)

Abstract

This paper presents new results on neurodynamic optimization approaches to robust pole assignment based on four alternative robustness measures. One or two recurrent neural networks are utilized to optimize these measures while making exact pole assignment. Compared with existing approaches, the present neurodynamic approaches can result in optimal robustness in most cases with one of the robustness measures. Simulation results of the proposed approaches for many benchmark problems are reported to demonstrate their performances. © 2013 IEEE.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
DOIs	https://doi.org/10.1109/IJCNN.2013.6706834
Publication status	Published - 2013
Externally published	Yes
Event	2013 International Joint Conference on Neural Networks, IJCNN 2013 - Dallas, United States Duration: 4 Aug 2013 → 9 Aug 2013 https://neural.memberclicks.net/assets/docs/2013_ijcnn_program_final.pdf

Conference

Conference	2013 International Joint Conference on Neural Networks, IJCNN 2013
Place	United States
City	Dallas
Period	4/08/13 → 9/08/13
Internet address	https://neural.memberclicks.net/assets/docs/2013_ijcnn_program_final.pdf

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@inproceedings{b6a9b8b72f7f403fa3340a7226db774d,

title = "Neurodynamic optimization approaches to robust pole assignment based on alternative robustness measures",

abstract = "This paper presents new results on neurodynamic optimization approaches to robust pole assignment based on four alternative robustness measures. One or two recurrent neural networks are utilized to optimize these measures while making exact pole assignment. Compared with existing approaches, the present neurodynamic approaches can result in optimal robustness in most cases with one of the robustness measures. Simulation results of the proposed approaches for many benchmark problems are reported to demonstrate their performances. {\textcopyright} 2013 IEEE.",

author = "Xinyi Le and Jun Wang",

year = "2013",

doi = "10.1109/IJCNN.2013.6706834",

language = "English",

isbn = "9781467361293",

booktitle = "Proceedings of the International Joint Conference on Neural Networks",

note = "2013 International Joint Conference on Neural Networks, IJCNN 2013 ; Conference date: 04-08-2013 Through 09-08-2013",

url = "https://neural.memberclicks.net/assets/docs/2013\_ijcnn\_program\_final.pdf",

}

TY - GEN

T1 - Neurodynamic optimization approaches to robust pole assignment based on alternative robustness measures

AU - Le, Xinyi

AU - Wang, Jun

PY - 2013

Y1 - 2013

N2 - This paper presents new results on neurodynamic optimization approaches to robust pole assignment based on four alternative robustness measures. One or two recurrent neural networks are utilized to optimize these measures while making exact pole assignment. Compared with existing approaches, the present neurodynamic approaches can result in optimal robustness in most cases with one of the robustness measures. Simulation results of the proposed approaches for many benchmark problems are reported to demonstrate their performances. © 2013 IEEE.

AB - This paper presents new results on neurodynamic optimization approaches to robust pole assignment based on four alternative robustness measures. One or two recurrent neural networks are utilized to optimize these measures while making exact pole assignment. Compared with existing approaches, the present neurodynamic approaches can result in optimal robustness in most cases with one of the robustness measures. Simulation results of the proposed approaches for many benchmark problems are reported to demonstrate their performances. © 2013 IEEE.

UR - https://www.scopus.com/pages/publications/84893615294

UR - https://www.scopus.com/pages/publications/84893615294#tab=impact

U2 - 10.1109/IJCNN.2013.6706834

DO - 10.1109/IJCNN.2013.6706834

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781467361293

BT - Proceedings of the International Joint Conference on Neural Networks

T2 - 2013 International Joint Conference on Neural Networks, IJCNN 2013

Y2 - 4 August 2013 through 9 August 2013

ER -

Neurodynamic optimization approaches to robust pole assignment based on alternative robustness measures

Abstract

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