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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.
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