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Filtering and System Identification - A Least Squares Approach (Hardcover)
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Filtering and System Identification - A Least Squares Approach (Hardcover)
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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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