00884nam--2200337---450-99000289379020331620070330171149.0000289379USA01000289379(ALEPH)000289379USA0100028937920070330d1937----km-y0itay50------bafreFR||||||||001yy<<L'>> affaire de West-PortMarie-Louise PailleronParisA. Michel[c.1937]252 p.20 cm20012001001-------2001843.9PAILLERON,Marie-Louise204152ITsalbcISBD990002893790203316XV.5. 710197517 LMXV.5.BKFGSENATORE9020070330USA011711Affaire de West-Port990174UNISA06379nam 2201309 450 991079036480332120220114023138.01-4008-3714-60-691-12098-610.1515/9781400837144(CKB)2670000000205180(EBL)1771114(SSID)ssj0000689660(PQKBManifestationID)12236421(PQKBTitleCode)TC0000689660(PQKBWorkID)10620242(PQKB)10423749(DE-B1597)446417(OCoLC)1004872417(OCoLC)1013938096(OCoLC)1049620158(DE-B1597)9781400837144(Au-PeEL)EBL1771114(CaPaEBR)ebr10915616(CaONFJC)MIL638822(OCoLC)889675022(MiAaPQ)EBC1771114(EXLCZ)99267000000020518020140904h20052005 uy 0engurnn#---|u||utxtccrGreen's function estimates for lattice Schrödinger operators and applications /J. BourgainPrinceton, New Jersey :Princeton University Press,2005.©20051 online resource (184 p.)Annals of Mathematics Studies ;Number 158Description based upon print version of record.1-322-07571-9 0-691-12097-8 Includes bibliographical references at the end of each chapters.Front matter --Contents --Acknowledgment --Chapter 1. Introduction --Chapter 2. Transfer Matrix and Lyapounov Exponent --Chapter 3. Herman's Subharmonicity Method --Chapter 4. Estimates on Subharmonic Functions --Chapter 5. LDT for Shift Model --Chapter 6. Avalanche Principle in SL2(R) --Chapter 7. Consequences for Lyapounov Exponent, IDS, and Green's Function --Chapter 8. Refinements --Chapter 9. Some Facts about Semialgebraic Sets --Chapter 10. Localization --Chapter 11. Generalization to Certain Long-Range Models --Chapter 12. Lyapounov Exponent and Spectrum --Chapter 13. Point Spectrum in Multifrequency Models at Small Disorder --Chapter 14. A Matrix-Valued Cartan-Type Theorem --Chapter 15. Application to Jacobi Matrices Associated with Skew Shifts --Chapter 16. Application to the Kicked Rotor Problem --Chapter 17. Quasi-Periodic Localization on the Zd-lattice (d > 1) --Chapter 18. An Approach to Melnikov's Theorem on Persistency of Nonresonant Lower Dimension Tori --Chapter 19. Application to the Construction of Quasi-Periodic Solutions of Nonlinear Schrödinger Equations --Chapter 20. Construction of Quasi-Periodic Solutions of Nonlinear Wave Equations --AppendixThis book presents an overview of recent developments in the area of localization for quasi-periodic lattice Schrödinger operators and the theory of quasi-periodicity in Hamiltonian evolution equations. The physical motivation of these models extends back to the works of Rudolph Peierls and Douglas R. Hofstadter, and the models themselves have been a focus of mathematical research for two decades. Jean Bourgain here sets forth the results and techniques that have been discovered in the last few years. He puts special emphasis on so-called "non-perturbative" methods and the important role of subharmonic function theory and semi-algebraic set methods. He describes various applications to the theory of differential equations and dynamical systems, in particular to the quantum kicked rotor and KAM theory for nonlinear Hamiltonian evolution equations. Intended primarily for graduate students and researchers in the general area of dynamical systems and mathematical physics, the book provides a coherent account of a large body of work that is presently scattered in the literature. It does so in a refreshingly contained manner that seeks to convey the present technological "state of the art."Annals of mathematics studies ;Number 158.Schrödinger operatorGreen's functionsHamiltonian systemsEvolution equationsAlmost Mathieu operator.Analytic function.Anderson localization.Betti number.Cartan's theorem.Chaos theory.Density of states.Dimension (vector space).Diophantine equation.Dynamical system.Equation.Existential quantification.Fundamental matrix (linear differential equation).Green's function.Hamiltonian system.Hermitian adjoint.Infimum and supremum.Iterative method.Jacobi operator.Linear equation.Linear map.Linearization.Monodromy matrix.Non-perturbative.Nonlinear system.Normal mode.Parameter space.Parameter.Parametrization.Partial differential equation.Periodic boundary conditions.Phase space.Phase transition.Polynomial.Renormalization.Self-adjoint.Semialgebraic set.Special case.Statistical significance.Subharmonic function.Summation.Theorem.Theory.Transfer matrix.Transversality (mathematics).Trigonometric functions.Trigonometric polynomial.Uniformization theorem.Schrödinger operator.Green's functions.Hamiltonian systems.Evolution equations.515.3/933.06bclBourgain Jean1954-56557MiAaPQMiAaPQMiAaPQBOOK9910790364803321Green's function estimates for lattice Schrödinger operators and applications3852862UNINA05647nam 22006974a 450 991083082830332120170815121300.01-280-26895-697866102689550-470-09015-41-60119-496-X0-470-09014-6(CKB)1000000000356580(EBL)232696(SSID)ssj0000071581(PQKBManifestationID)11107242(PQKBTitleCode)TC0000071581(PQKBWorkID)10091241(PQKB)10032024(MiAaPQ)EBC232696(CaSebORM)9780470090138(OCoLC)85820614(PPN)115219536(EXLCZ)99100000000035658020040517d2004 uy 0engur|n|---|||||txtccrClassification, parameter estimation, and state estimation[electronic resource] an engineering approach using MATLAB /F. van der Heijden ... [et al.]1st editionChichester, West Sussex, Eng. ;Hoboken, NJ Wileyc20041 online resource (441 p.)Description based upon print version of record.0-470-09013-8 Includes bibliographical references and index.Classification, Parameter Estimation and State Estimation; Contents; Preface; Foreword; 1 Introduction; 1.1 The scope of the book; 1.1.1 Classification; 1.1.2 Parameter estimation; 1.1.3 State estimation; 1.1.4 Relations between the subjects; 1.2 Engineering; 1.3 The organization of the book; 1.4 References; 2 Detection and Classification; 2.1 Bayesian classification; 2.1.1 Uniform cost function and minimum error rate; 2.1.2 Normal distributed measurements; linear and quadratic classifiers; 2.2 Rejection; 2.2.1 Minimum error rate classification with reject option2.3 Detection: the two-class case2.4 Selected bibliography; 2.5 Exercises; 3 Parameter Estimation; 3.1 Bayesian estimation; 3.1.1 MMSE estimation; 3.1.2 MAP estimation; 3.1.3 The Gaussian case with linear sensors; 3.1.4 Maximum likelihood estimation; 3.1.5 Unbiased linear MMSE estimation; 3.2 Performance of estimators; 3.2.1 Bias and covariance; 3.2.2 The error covariance of the unbiased linear MMSE estimator; 3.3 Data fitting; 3.3.1 Least squares fitting; 3.3.2 Fitting using a robust error norm; 3.3.3 Regression; 3.4 Overview of the family of estimators; 3.5 Selected bibliography3.6 Exercises4 State Estimation; 4.1 A general framework for online estimation; 4.1.1 Models; 4.1.2 Optimal online estimation; 4.2 Continuous state variables; 4.2.1 Optimal online estimation in linear-Gaussian systems; 4.2.2 Suboptimal solutions for nonlinear systems; 4.2.3 Other filters for nonlinear systems; 4.3 Discrete state variables; 4.3.1 Hidden Markov models; 4.3.2 Online state estimation; 4.3.3 Offline state estimation; 4.4 Mixed states and the particle filter; 4.4.1 Importance sampling; 4.4.2 Resampling by selection; 4.4.3 The condensation algorithm; 4.5 Selected bibliography4.6 Exercises5 Supervised Learning; 5.1 Training sets; 5.2 Parametric learning; 5.2.1 Gaussian distribution, mean unknown; 5.2.2 Gaussian distribution, covariance matrix unknown; 5.2.3 Gaussian distribution, mean and covariance matrix both unknown; 5.2.4 Estimation of the prior probabilities; 5.2.5 Binary measurements; 5.3 Nonparametric learning; 5.3.1 Parzen estimation and histogramming; 5.3.2 Nearest neighbour classification; 5.3.3 Linear discriminant functions; 5.3.4 The support vector classifier; 5.3.5 The feed-forward neural network; 5.4 Empirical evaluation; 5.5 References5.6 Exercises6 Feature Extraction and Selection; 6.1 Criteria for selection and extraction; 6.1.1 Inter/intra class distance; 6.1.2 Chernoff-Bhattacharyya distance; 6.1.3 Other criteria; 6.2 Feature selection; 6.2.1 Branch-and-bound; 6.2.2 Suboptimal search; 6.2.3 Implementation issues; 6.3 Linear feature extraction; 6.3.1 Feature extraction based on the Bhattacharyya distance with Gaussian distributions; 6.3.2 Feature extraction based on inter/intra class distance; 6.4 References; 6.5 Exercises; 7 Unsupervised Learning; 7.1 Feature reduction; 7.1.1 Principal component analysis7.1.2 Multi-dimensional scalingClassification, Parameter Estimation and State Estimation is a practical guide for data analysts and designers of measurement systems and postgraduates students that are interested in advanced measurement systems using MATLAB. 'Prtools' is a powerful MATLAB toolbox for pattern recognition and is written and owned by one of the co-authors, B. Duin of the Delft University of Technology. After an introductory chapter, the book provides the theoretical construction for classification, estimation and state estimation. The book also deals with the skills required to bring the theoretical coEngineering mathematicsData processingMeasurementData processingEstimation theoryData processingEngineering mathematicsData processing.MeasurementData processing.Estimation theoryData processing.620.0015118681/.2Duin Robert951627Heijden Ferdinand van der951628MiAaPQMiAaPQMiAaPQBOOK9910830828303321Classification, parameter estimation, and state estimation2151342UNINA