A Neurodynamic Optimization Approach to Synthesis of Linear Systems with Fault Detection via Robust Pole Assignment
Refereed conference paper presented and published in conference proceedings

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AbstractThis paper presents a neurodynamic optimization approach with two coupled recurrent neural networks for the synthesis of linear systems with fault detection via robust pole assignment. The proposed approach is shown to be capable of synthesizing control systems with robust state estimators and fault detection with parameter perturbation. The operating characteristics of the recurrent neural networks for state estimation and fault detection are demonstrated by using an illustrative example.
All Author(s) ListLe XY, Wang J
Name of ConferenceInternational Joint Conference on Neural Networks (IJCNN)
Start Date of Conference12/07/2015
End Date of Conference17/07/2015
Place of ConferenceKillarney
Country/Region of ConferenceIreland
Detailed descriptionorganized by International Neural Network Society,
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Engineering; Engineering, Electrical & Electronic

Last updated on 2021-15-09 at 04:29