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

Xinyi Le, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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.

Original languageEnglish
Title of host publication2013 International Joint Conference on Neural Networks, IJCNN 2013
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 International Joint Conference on Neural Networks, IJCNN 2013 - Dallas, TX, United States
Duration: 4 Aug 20139 Aug 2013

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2013 International Joint Conference on Neural Networks, IJCNN 2013
Country/TerritoryUnited States
CityDallas, TX
Period4/08/139/08/13

Fingerprint

Dive into the research topics of 'Neurodynamic optimization approaches to robust pole assignment based on alternative robustness measures'. Together they form a unique fingerprint.

Cite this