Download or read book SYNTHESE D UNE COMMANDE ROBUSTE LIEE A LA QUALITE DE L IDENTIFICATION BAYESIENNE written by GERALDO.. GOMES and published by . This book was released on 1991 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: DANS CETTE THESE, NOUS PROPOSONS UNE NOUVELLE SYNTHESE DE COMMANDE EN BOUCLE FERMEE, APPLIQUEE A DES SYSTEMES LINEAIRES DISCRETS ET ROBUSTES PAR RAPPORT A UN ENSEMBLE DE PARAMETRES INCERTAINS DU SYSTEME. CETTE NOUVELLE MESURE DE ROBUSTESSE EST BASEE SUR LES LIAISONS EXISTANTES ENTRE UNE MAUVAISE QUALITE DE L'IDENTIFICATION PARAMETRIQUE EN BOUCLE FERMEE ET LA BONNE ROBUSTESSE EN STABILITE ET PERFORMANCE DU REGULATEUR. AUTREMENT DIT, MEILLEURE EST LA COMMANDE PLUS MAUVAISE EST LA QUALITE DE L'IDENTIFICATION. CETTE CONDITION EST IMPORTANTE CAR IL N'EST PAS QUESTION D'IDENTIFIER EN TEMPS REEL OU DIFFERE LES PARAMETRES INCERTAINS DU SYSTEME, MAIS D'UTILISER LES PROPRIETES DU COMPORTEMENT DES ESTIMES EN REGIME ASYMPTOTIQUE. NOUS MONTRONS LES LIENS TRES ETROITS AVEC LA METHODE PRLQG ET CELLE-CI APPARAIT COMME UN CAS PARTICULIER DE LA TECHNIQUE PRCBI. LES RESULTATS PRESENTES SURTOUT L'APPLICATION A UNE STRUCTURE FLEXIBLE MONTRENT QUE CETTE NOUVELLE METHODE PRESENTE UNE EXCELLENTE ROBUSTESSE EN STABILITE ET EN PERFORMANCE

Download or read book Techniques de commande robuste written by Christelle Manceaux-Cumer and published by . This book was released on 1998 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cette thèse concerne l'analyse et la synthèse de commandes, capables de stabiliser des systèmes linéaires en dépit d'incertitudes fréquentielles et paramétriques. Les travaux sont essentiellement axés sur la mise en œuvre pratique des méthodes de commande robuste. Mais, la prise en compte de la nature et de la structure de l'incertitude ainsi que le niveau de suffisance du critère de stabilité choisi rendent ce problème difficile à résoudre. Les méthodes couramment utilisées sont récapitulées mais ne répondent que partiellement au problème d'analyse. En particulier, les critères de stabilité mis sous forme de tests fréquentiels souffrent de l'échantillonnage fréquentiel et ne peuvent qu'induire des erreurs lors de la synthèse (μ-analyse et μ-synthèse). L'obtention de critères de stabilité pratiques pour la synthèse a donc orienté les travaux dans deux directions complémentaires. Le premier axe de recherche élabore des algorithmes fondés sur la théorie des multiplicateurs pour traiter des problèmes de type μ-synthèse (ou synthèse H2 robuste) dans lesquels les incertitudes peuvent être mixtes. Là encore, nous utilisons une heuristique classique qui alterne entre analyse, à correcteur fixé, et synthèse, à multiplicateur fixé mais qui présente l'avantage d'éviter l'échantillonnage en fréquences. Au préalable, les multiplicateurs sont choisis dans une base soit polynômiale soit rationnelle. Ces approches se formulent par des problèmes d'optimisation convexe, de type LMI. Le scond axe de recherche utilise une technqiue hybride identification / commande pour le calcul des marges paramétriques d'un système commandé (PRABI), fondée sur la qualité d'identification paramétrique. Cette technique est exploitée pour l'obtention d'un nouvel algotithme de désensibilisation. On robustifie itérativement un compensateur à structure donnée, tout en assurant un compromis performance / robustesse paramétrique. Cela nous amène à travailler successivement dans l'espace des paramètres définissant le correcteur, puis dans l'espace des paramètres inhérents au modèle. En fin, on propose une généralisation de la méthode d'analyse PRABI, nécessitant le calcul d'un correcteur LQG équivalent. Toutes ces techniques sont validées sur des cas tests et des problèmes réalistes de l'aéronautique (missile, satellite de télécommunication).

Download or read book UKACC International Conference on Control 98 1 4 September 1998 Venue University of Wales Swansea UK written by and published by . This book was released on 1998 with total page 902 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Neural Networks written by Gérard Dreyfus and published by Springer Science & Business Media. This book was released on 2005-11-25 with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts and edited to present a coherent and comprehensive, yet not redundant, practically oriented introduction.

Download or read book Predicting Structured Data written by Neural Information Processing Systems Foundation and published by MIT Press. This book was released on 2007 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.

Download or read book An Introduction to Computational Learning Theory written by Michael J. Kearns and published by MIT Press. This book was released on 1994-08-15 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.

Download or read book The Nature of Statistical Learning Theory written by Vladimir Vapnik and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.

Download or read book CIKM 13 written by CIKM 13 Conference Committee and published by . This book was released on 2013-10-27 with total page 938 pages. Available in PDF, EPUB and Kindle. Book excerpt: CIKM'13: 22nd ACM International Conference on Information and Knowledge Management Oct 27, 2013-Nov 01, 2013 San Francisco, USA. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.

Download or read book Reinforcement Learning second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Download or read book Risk and Reliability in Geotechnical Engineering written by Kok-Kwang Phoon and published by CRC Press. This book was released on 2018-10-09 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Establishes Geotechnical Reliability as Fundamentally Distinct from Structural Reliability Reliability-based design is relatively well established in structural design. Its use is less mature in geotechnical design, but there is a steady progression towards reliability-based design as seen in the inclusion of a new Annex D on "Reliability of Geotechnical Structures" in the third edition of ISO 2394. Reliability-based design can be viewed as a simplified form of risk-based design where different consequences of failure are implicitly covered by the adoption of different target reliability indices. Explicit risk management methodologies are required for large geotechnical systems where soil and loading conditions are too varied to be conveniently slotted into a few reliability classes (typically three) and an associated simple discrete tier of target reliability indices. Provides Realistic Practical Guidance Risk and Reliability in Geotechnical Engineering makes these reliability and risk methodologies more accessible to practitioners and researchers by presenting soil statistics which are necessary inputs, by explaining how calculations can be carried out using simple tools, and by presenting illustrative or actual examples showcasing the benefits and limitations of these methodologies. With contributions from a broad international group of authors, this text: Presents probabilistic models suited for soil parameters Provides easy-to-use Excel-based methods for reliability analysis Connects reliability analysis to design codes (including LRFD and Eurocode 7) Maximizes value of information using Bayesian updating Contains efficient reliability analysis methods Accessible To a Wide Audience Risk and Reliability in Geotechnical Engineering presents all the "need-to-know" information for a non-specialist to calculate and interpret the reliability index and risk of geotechnical structures in a realistic and robust way. It suits engineers, researchers, and students who are interested in the practical outcomes of reliability and risk analyses without going into the intricacies of the underlying mathematical theories.

Download or read book Basics and Trends in Sensitivity Analysis Theory and Practice in R written by Sébastien Da Veiga and published by SIAM. This book was released on 2021-10-14 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of global sensitivity analysis methods and algorithms, including their theoretical basis and mathematical properties. The authors use a practical point of view and real case studies as well as numerous examples, and applications of the different approaches are illustrated throughout using R code to explain their usage and usefulness in practice. Basics and Trends in Sensitivity Analysis: Theory and Practice in R covers a lot of material, including theoretical aspects of Sobol’ indices as well as sampling-based formulas, spectral methods, and metamodel-based approaches for estimation purposes; screening techniques devoted to identifying influential and noninfluential inputs; variance-based measures when model inputs are statistically dependent (and several other approaches that go beyond variance-based sensitivity measures); and a case study in R related to a COVID-19 epidemic model where the full workflow of sensitivity analysis combining several techniques is presented. This book is intended for engineers, researchers, and undergraduate students who use complex numerical models and have an interest in sensitivity analysis techniques and is appropriate for anyone with a solid mathematical background in basic statistical and probability theories who develops and uses numerical models in all scientific and engineering domains.

Download or read book Ultra Wide Band Antennas written by Xavier Begaud and published by John Wiley & Sons. This book was released on 2013-03-04 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ultra Wide Band Technology (UWB) has reached a level of maturity that allows us to offer wireless links with either high or low data rates. These wireless links are frequently associated with a location capability for which ultimate accuracy varies with the inverse of the frequency bandwidth. Using time or frequency domain waveforms, they are currently the subject of international standards facilitating their commercial implementation. Drawing up a complete state of the art, Ultra Wide Band Antennas is aimed at students, engineers and researchers and presents a summary of internationally recognized studies.

Download or read book Monte Carlo and Quasi Monte Carlo Methods written by Ronald Cools and published by Springer. This book was released on 2016-06-13 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the refereed proceedings of the Eleventh International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing that was held at the University of Leuven (Belgium) in April 2014. These biennial conferences are major events for Monte Carlo and quasi-Monte Carlo researchers. The proceedings include articles based on invited lectures as well as carefully selected contributed papers on all theoretical aspects and applications of Monte Carlo and quasi-Monte Carlo methods. Offering information on the latest developments in these very active areas, this book is an excellent reference resource for theoreticians and practitioners interested in solving high-dimensional computational problems, arising, in particular, in finance, statistics and computer graphics.

Download or read book Markov Decision Processes in Artificial Intelligence written by Olivier Sigaud and published by John Wiley & Sons. This book was released on 2013-03-04 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as reinforcement learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in artificial intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, reinforcement learning, partially observable MDPs, Markov games and the use of non-classical criteria). It then presents more advanced research trends in the field and gives some concrete examples using illustrative real life applications.

Download or read book Model Reduction and Approximation written by Peter Benner and published by SIAM. This book was released on 2017-07-06 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many physical, chemical, biomedical, and technical processes can be described by partial differential equations or dynamical systems. In spite of increasing computational capacities, many problems are of such high complexity that they are solvable only with severe simplifications, and the design of efficient numerical schemes remains a central research challenge. This book presents a tutorial introduction to recent developments in mathematical methods for model reduction and approximation of complex systems. Model Reduction and Approximation: Theory and Algorithms contains three parts that cover (I) sampling-based methods, such as the reduced basis method and proper orthogonal decomposition, (II) approximation of high-dimensional problems by low-rank tensor techniques, and (III) system-theoretic methods, such as balanced truncation, interpolatory methods, and the Loewner framework. It is tutorial in nature, giving an accessible introduction to state-of-the-art model reduction and approximation methods. It also covers a wide range of methods drawn from typically distinct communities (sampling based, tensor based, system-theoretic).?? This book is intended for researchers interested in model reduction and approximation, particularly graduate students and young researchers.

Download or read book MIMO written by Alain Sibille and published by Academic Press. This book was released on 2010-12-03 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foreword from Arogyaswami Paulraj, Professor (Emeritus), Stanford University (USA) The first book to show how MIMO principles can be implemented in today’s mobile broadband networks and components Explains and solves some of the practical difficulties that arise in designing and implementing MIMO systems Both theory and implementation sections are written in the context of the most recent standards: IEEE 802.11n (WiFi); IEEE 802.16 (WIMAX); 4G networks (3GPP/3GPP2, LTE)

Download or read book Dependence Modeling written by Harry Joe and published by World Scientific. This book was released on 2011 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: 1. Introduction : Dependence modeling / D. Kurowicka -- 2. Multivariate copulae / M. Fischer -- 3. Vines arise / R.M. Cooke, H. Joe and K. Aas -- 4. Sampling count variables with specified Pearson correlation : A comparison between a naive and a C-vine sampling approach / V. Erhardt and C. Czado -- 5. Micro correlations and tail dependence / R.M. Cooke, C. Kousky and H. Joe -- 6. The Copula information criterion and Its implications for the maximum pseudo-likelihood estimator / S. Gronneberg -- 7. Dependence comparisons of vine copulae with four or more variables / H. Joe -- 8. Tail dependence in vine copulae / H. Joe -- 9. Counting vines / O. Morales-Napoles -- 10. Regular vines : Generation algorithm and number of equivalence classes / H. Joe, R.M. Cooke and D. Kurowicka -- 11. Optimal truncation of vines / D. Kurowicka -- 12. Bayesian inference for D-vines : Estimation and model selection / C. Czado and A. Min -- 13. Analysis of Australian electricity loads using joint Bayesian inference of D-vines with autoregressive margins / C. Czado, F. Gartner and A. Min -- 14. Non-parametric Bayesian belief nets versus vines / A. Hanea -- 15. Modeling dependence between financial returns using pair-copula constructions / K. Aas and D. Berg -- 16. Dynamic D-vine model / A. Heinen and A. Valdesogo -- 17. Summary and future directions / D. Kurowicka