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Autor(en): 
  • Giorgio Picci
  • Anders Lindquist
  • Linear Stochastic Systems: A Geometric Approach to Modeling, Estimation and Identification 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Oktober 2016  
    Genre:  Schulbücher 
     
    Automatic control engineering / B / Control and Systems Theory / Control engineering / Mathematics and Statistics / Probabilities / Probability & statistics / Probability Theory / Probability Theory and Stochastic Processes / Stochastics / System Theory / Systems Theory, Control
    ISBN:  9783662526187 
    EAN-Code: 
    9783662526187 
    Verlag:  Springer Nature EN 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  #01 - Series in Contemporary Mathematics  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Gewicht:  11869 gr 
    Seiten:  781 
    Illustration:  XV, 781 p. 37 illus., schwarz-weiss Illustrationen 
    Zus. Info:  Previously published in hardcover 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    This book presents a treatise on the theory and modeling of second-order stationary processes, including an exposition on selected application areas that are important in the engineering and applied sciences. The foundational issues regarding stationary processes dealt with in the beginning of the book have a long history, starting in the 1940s with the work of Kolmogorov, Wiener, Cramér and his students, in particular Wold, and have since been refined and complemented by many others. Problems concerning the filtering and modeling of stationary random signals and systems have also been addressed and studied, fostered by the advent of modern digital computers, since the fundamental work of R.E. Kalman in the early 1960s. The book offers a unified and logically consistent view of the subject based on simple ideas from Hilbert space geometry and coordinate-free thinking. In this framework, the concepts of stochastic state space and state space modeling, based on the notionof the conditional independence of past and future flows of the relevant signals, are revealed to be fundamentally unifying ideas. The book, based on over 30 years of original research, represents a valuable contribution that will inform the fields of stochastic modeling, estimation, system identification, and time series analysis for decades to come. It also provides the mathematical tools needed to grasp and analyze the structures of algorithms in stochastic systems theory.
      
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