Open AccessBook
Stability of Time-Delay Systems
TLDR
Preface, Notations 1.Introduction to Time-Delay Systems I.Robust Stability Analysis II.Input-output stability A.LMI and Quadratic Integral Inequalities Bibliography IndexAbstract:
Preface, Notations 1.Introduction to Time-Delay Systems I.Frequency-Domain Approach 2.Systems with Commensurate Delays 3.Systems withIncommensurate Delays 4.Robust Stability Analysis II.Time Domain Approach 5.Systems with Single Delay 6.Robust Stability Analysis 7.Systems with Multiple and Distributed Delays III.Input-Output Approach 8.Input-output stability A.Matrix Facts B.LMI and Quadratic Integral Inequalities Bibliography Indexread more
Citations
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Journal ArticleDOI
Delay-interval-dependent stability of recurrent neural networks with time-varying delay
Chuandong Li,Gang Feng +1 more
TL;DR: The present results, together with two numerical examples, show that the equilibrium points of the considered networks may be globally asymptotically stable in some delay interval(s) even though the equilibriumpoints of the corresponding delay-free recurrent neural networks are not globally asynchotic stable.
Journal ArticleDOI
Mode-Dependent Stochastic Synchronization for Markovian Coupled Neural Networks With Time-Varying Mode-Delays
TL;DR: A mode-dependent augmented Lyapunov-Krasovskii functional is proposed, where some terms involving triple or quadruple integrals are considered, which gives significant improvement in the synchronization criteria, i.e., less conservative results can be obtained.
Journal ArticleDOI
Global asymptotic stability of stochastic BAM neural networks with distributed delays and reaction-diffusion terms
TL;DR: The linear matrix inequality (LMI) method is applied to propose some new sufficient stability conditions for reaction-diffusion stochastic BAM neural networks with discrete and distributed delays to improve upon the existing stability results.
Journal ArticleDOI
Polynomial-Type Lyapunov–Krasovskii Functional and Jacobi–Bessel Inequality: Further Results on Stability Analysis of Time-Delay Systems
TL;DR: This article generalizes the results of previous literature by proposing a polynomial-type LKF, which contains the LKFs with multiple integral terms as special cases, and presents a Jacobi–Bessel inequality to bound the derivative of such LKf.
Journal ArticleDOI
Guaranteed $H_{\infty}$ Performance State Estimation of Delayed Static Neural Networks
TL;DR: Compared with some previous results, much better performance is achieved by the Arcak-type state estimator, which is greatly benefited from introducing an additional gain matrix in the domain of activation function.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Book
Computers and Intractability: A Guide to the Theory of NP-Completeness
TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Book
Matrix Analysis
Roger A. Horn,Charles R. Johnson +1 more
TL;DR: In this article, the authors present results of both classic and recent matrix analyses using canonical forms as a unifying theme, and demonstrate their importance in a variety of applications, such as linear algebra and matrix theory.