NDLI: <inline-formula> <tex-math notation=LaTeX>$H_{infty }$ </tex-math></inline-formula> State Estimation for Discrete-Time Delayed Systems of the Neural Network Type With Multiple Missing Measurements

Content Provider	IEEE Xplore Digital Library
Author	Meiqin Liu Haiyang Chen
Copyright Year	2012
Abstract	This paper investigates the H state estimation problem for a class of discrete-time nonlinear systems of the neural network type with random time-varying delays and multiple missing measurements. These nonlinear systems include recurrent neural networks, complex network systems, Lur'e systems, and so on which can be described by a unified model consisting of a linear dynamic system and a static nonlinear operator. The missing phenomenon commonly existing in measurements is assumed to occur randomly by introducing mutually individual random variables satisfying certain kind of probability distribution. Throughout this paper, first a Luenberger-like estimator based on the imperfect output data is constructed to obtain the immeasurable system states. Then, by virtue of Lyapunov stability theory and stochastic method, the H_∞ performance of the estimation error dynamical system (augmented system) is analyzed. Based on the analysis, the H_∞ estimator gains are deduced such that the augmented system is globally mean square stable. In this paper, both the variation range and distribution probability of the time delay are incorporated into the control laws, which allows us to not only have more accurate models of the real physical systems, but also obtain less conservative results. Finally, three illustrative examples are provided to validate the proposed control laws.
Page Count	12
File Size	1191204
Starting Page	2987
Ending Page	2998
File Format	PDF
ISSN	2162237X
Volume Number	26
Issue Number	12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Delays Neural networks Symmetric matrices State estimation Loss measurement Nonlinear systems Delay effects time-varying delays. Delay-distribution H∞ state estimator multiple missing measurements stochastic analysis time-varying delays $H_{\infty }$ state estimator
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

$H_{infty}$ State Estimation for Discrete-Time Complex Networks With Randomly Occurring Sensor Saturations and Randomly Varying Sensor Delays

$$H_\infty $$ state estimation of stochastic neural networks with mixed time-varying delays

Dissipativity Analysis for Discrete-Time Stochastic Neural Networks With Time-Varying Delays

Variance-constrained resilient (H_{infty }) state estimation for time-varying neural networks with randomly varying nonlinearities and missing measurements

Exponential $mathcal {H}_{infty }$ Filtering for Discrete-Time Switched Neural Networks With Random Delays

Improved results on (mathcal{H}_{infty}) state estimation of static neural networks with interval time-varying delay

$H_{infty}$ Filtering for Discrete-Time Systems With Stochastic Incomplete Measurement and Mixed Delays

springerOpen/ (H_{infty}) State estimation for discrete-time neural networks with time-varying and distributed delays

New results on (H_{infty}) state estimation of static neural networks with time-varying delay

$H_{infty }$ State Estimation for Discrete-Time Delayed Systems of the Neural Network Type With Multiple Missing Measurements

Similar Documents

$H_{infty}$ State Estimation for Discrete-Time Complex Networks With Randomly Occurring Sensor Saturations and Randomly Varying Sensor Delays

$$H_\infty $$ state estimation of stochastic neural networks with mixed time-varying delays

Dissipativity Analysis for Discrete-Time Stochastic Neural Networks With Time-Varying Delays

Variance-constrained resilient (H_{infty }) state estimation for time-varying neural networks with randomly varying nonlinearities and missing measurements

Exponential $mathcal {H}_{infty }$ Filtering for Discrete-Time Switched Neural Networks With Random Delays

Improved results on (mathcal{H}_{infty}) state estimation of static neural networks with interval time-varying delay

$H_{infty}$ Filtering for Discrete-Time Systems With Stochastic Incomplete Measurement and Mixed Delays

springerOpen/ (H_{infty}) State estimation for discrete-time neural networks with time-varying and distributed delays

New results on (H_{infty}) state estimation of static neural networks with time-varying delay

$H_{infty }$ State Estimation for Discrete-Time Delayed Systems of the Neural Network Type With Multiple Missing Measurements