rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Adaptive filtering prediction and control
  name:
    name part: Goodwin, Graham C. (Graham Clifford)
    name part: 1945-
    role:
      role term: creator
  name:
    name part: Sin, Kwai Sang
    name part: 1952-
  type of resource: text
  genre: bibliography
  genre: elektronické knihy
  genre: electronic books
  origin info:
    place:
      place term: nju
    place:
      place term: Englewood Cliffs, N.J
    publisher: Prentice-Hall
    date issued: ©1984
    date issued: 1984
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xii, 540 pages) : illustrations
  abstract: This unified survey of the theory of adaptive filtering, prediction, and control focuses on linear discrete-time systems and explores the natural extensions to nonlinear systems. In keeping with the importance of computers to practical applications, the authors emphasize discrete-time systems. Their approach summarizes the theoretical and practical aspects of a large class of adaptive algorithms. Ideal for advanced undergraduate and graduate classes, this treatment consists of two parts. The first section concerns deterministic systems, covering models, parameter estimation, and adaptive prediction and control. The second part examines stochastic systems, exploring optimal filtering and prediction, parameter estimation, adaptive filtering and prediction, and adaptive control. Extensive appendices offer a summary of relevant background material, making this volume largely self-contained. Readers will find that these theories, formulas, and applications are related to a variety of fields, including biotechnology, aerospace engineering, computer sciences, and electrical engineering.
  table of contents: Cover; Title Page; Copyright Page; Table of Contents; Preface; 1 Introduction To Adaptive Techniques; 1.1 Filtering; 1.2 Prediction; 1.3 Control; Part I: Deterministic Systems; 2 Models for Deterministic Dynamical Systems; 2.1 Introduction; 2.2 State-Space Models; 2.2.1 General; 2.2.2 Controllable State-Space Models; 2.2.3 Observable State-Space Models; 2.2.4 Minimal State-Space Models; 2.3 Difference Operator Representations; 2.3.1 General; 2.3.2 Right Difference Operator Representations; 2.3.3 Left Difference Operator Representations; 2.3.4 Deterministic Autoregressive Moving-Average Models.
  table of contents: 2.3.5 Irreducible Difference Operator Representations2.4 Models for Bilinear Systems; 3 Parameter Estimation for Deterministic Systems; 3.1 Introduction; 3.2 On-Line Estimation Schemes; 3.3 Equation Error Methods for Deterministic Systems; 3.4 Parameter Convergence; 3.4.1 The Orthogonalized Projection Algorithm; 3.4.2 The Least-Squares Algorithm; 3.4.3 The Projection Algorithm; 3.4.4 Persistent Excitation; 3.5 Output Error Methods; 3.6 Parameter Estimation with Bounded Noise; 3.7 Constrained Parameter Estimation; 3.8 Parameter Estimation for Multi-output Systems; 3.9 Concluding Remarks.
  table of contents: 4 Deterministic Adaptive Prediction4.1 Introduction; 4.2 Predictor Structures; 4.2.1 Prediction with Known Models; 4.2.2 Restricted Complexity Predictors; 4.3 Adaptive Prediction; 4.3.1 Direct Adaptive Prediction; 4.3.2 Indirect Adaptive Prediction; 4.4 Concluding Remarks; 5 Control of Linear Deterministic Systems; 5.1 Introduction; 5.2 Minimum Prediction Error Controllers; 5.2.1 One-Step-Ahead Control (The SISO Case); 5.2.2 Model Reference Control (The SISO Case); 5.2.3 One-Step-Ahead Design for Multi-input Multi-output Systems; 5.2.4 Robustness Considerations.
  table of contents: 5.3 Closed-Loop Pole Assignment5.3.1 Introduction; 5.3.2 The Pole Assignment Algorithm (Difference Operator Formulation); 5.3.3 Rapprochement with State- Variable Feedback; 5.3.4 Rapprochement with Minimum Prediction Error Control; 5.3.5 The Internal Model Principle; 5.3.6 Some Design Considerations; 5.4 An Illustrative Example; 6 Adaptive Control Of Linear Deterministic Systems; 6.1 Introduction; 6.2 The Key Technical Lemma; 6.3 Minimum Prediction Error Adaptive Controllers (Direct Approach); 6.3.1 One-Step-Ahead Adaptive Control (The SISO Case); 6.3.2 Model Reference Adaptive Control.
  table of contents: 6.3.3 One-Step-Ahead Adaptive Controllers for Multi-input Multi-output Systems6.4 Minimum Prediction Error Adaptive Controllers (Indirect Approach); 6.5 Adaptive Algorithms for Closed-Loop Pole Assignment; 6.6 Adaptive Control of Nonlinear Systems; 6.7 Adaptive Control of Time-Varying Systems; 6.8 Some Implementation Considerations; Part II: Stochastic Systems; 7 Optimal Filtering and Prediction; 7.1 Introduction; 7.2 Stochastic State-Space Models; 7.3 Linear Optimal Filtering and Prediction; 7.3.1 The Kalman Filter; 7.3.2 Fixed-Lag Smoothing; 7.3.3 Fixed-Point Smoothing.
  note: Graham C. Goodwin and Kwai Sang Sin.
  note: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  note: Includes bibliographical references (pages 516-534) and index.
  subject:
    topic: Discrete-time systems
  subject:
    topic: Filters (Mathematics)
  subject:
    topic: Prediction theory
  subject:
    topic: Control theory
  related item:
    title info:
      title: Adaptive filtering prediction and control
    name:
      name part: Goodwin, Graham C. (Graham Clifford), 1945-
    origin info:
      publisher: Englewood Cliffs, N.J. : Prentice-Hall, ©1984
    identifier: (DLC) 83023023
    identifier: (OCoLC)10183440
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  access condition: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  record info:
    record content source: OCLCE
    record creation date: 111201
    record change date: 20240717213016.0
    record identifier: kn-ocn765641472
    language of cataloging:
      language term: eng