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  3. Filtering and System Identification
  • Cited by 470
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    • Publisher:
      Cambridge University Press
      Publication date:
      January 2010
      April 2007
      ISBN:
      9780511618888
      9780521875127
      9781107405028
      DOI:
      https://doi.org/10.1017/CBO9780511618888
      Dimensions:
      (247 x 174 mm)
      Weight & Pages:
      0.98kg, 422 Pages
      Dimensions:
      (244 x 170 mm)
      Weight & Pages:
      0.67kg, 422 Pages
      • Subjects:
      Mathematics, Differential and Integral Equations, Dynamical Systems and Control Theory, Engineering, Control Systems and Optimisation
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    Subjects:
    Mathematics, Differential and Integral Equations, Dynamical Systems and Control Theory, Engineering, Control Systems and Optimisation
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    Book description

    Filtering and system identification are powerful techniques for building models of complex systems. This 2007 book discusses the design of reliable numerical methods to retrieve missing information in models derived using these techniques. Emphasis is on the least squares approach as applied to the linear state-space model, and problems of increasing complexity are analyzed and solved within this framework, starting with the Kalman filter and concluding with the estimation of a full model, noise statistics and state estimator directly from the data. Key background topics, including linear matrix algebra and linear system theory, are covered, followed by different estimation and identification methods in the state-space model. With end-of-chapter exercises, MATLAB simulations and numerous illustrations, this book will appeal to graduate students and researchers in electrical, mechanical and aerospace engineering. It is also useful for practitioners. Additional resources for this title, including solutions for instructors, are available online at www.cambridge.org/9780521875127.

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