Principles of adaptive filters and self-learning systems

Author(s)

    • Zaknich, Anthony

Bibliographic Information

Principles of adaptive filters and self-learning systems

A. Zaknich

(Advanced textbooks in control and signal processing)

Springer, 2005

  • : pbk

Available at  / 8 libraries

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Note

Includes bibliographical references and index

Description and Table of Contents

Description

Teaches students about classical and nonclassical adaptive systems within one pair of covers Helps tutors with time-saving course plans, ready-made practical assignments and examination guidance The recently developed "practical sub-space adaptive filter" allows the reader to combine any set of classical and/or non-classical adaptive systems to form a powerful technology for solving complex nonlinear problems

Table of Contents

Part I: Introduction Adaptive Filtering Linear Systems and Stochastic Processes Part II: Modelling Optimisation and Least Square Estimation Parametric Signal and System Modelling Part III: Classical Filters and Spectral Analysis Optimum Wiener Filter Optimal Kalman Filter Power Spectral Density Analysis Part IV: Adaptive Filter Theory Adaptive Finite Impulse Response Filters Frequency Domain Adaptive Filters Adaptive Volterra Filters Adaptive Control Systems Part V: Nonclassical Adaptive Systems Introduction to Neural Networks Introduction to Fuzzy Logic Systems Introduction to Genetic Algorithms Part VI: Adaptive Filter Application Applications of Adaptive Signal Processing Generic Adaptive Filter Structures

by "Nielsen BookData"

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Details

  • NCID
    BA72132918
  • ISBN
    • 1852339845
  • Country Code
    uk
  • Title Language Code
    eng
  • Text Language Code
    eng
  • Place of Publication
    London
  • Pages/Volumes
    xxii, 386 p.
  • Size
    24 cm
  • Parent Bibliography ID
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