NONLINEAR FILTERS Discover the utility of using deep learning and
(deep) reinforcement learning in deriving filtering algorithms with
this insightful and powerful new resource Nonlinear Filters: Theory
and Applications delivers an insightful view on state and parameter
estimation by merging ideas from control theory, statistical signal
processing, and machine learning. Taking an algorithmic approach,
the book covers both classic and machine learning-based filtering
algorithms. Readers of Nonlinear Filters will greatly benefit from
the wide spectrum of presented topics including stability,
robustness, computability, and algorithmic sufficiency. Readers
will also enjoy: Organization that allows the book to act as a
stand-alone, self-contained reference A thorough exploration of the
notion of observability, nonlinear observers, and the theory of
optimal nonlinear filtering that bridges the gap between different
science and engineering disciplines A profound account of Bayesian
filters including Kalman filter and its variants as well as
particle filter A rigorous derivation of the smooth variable
structure filter as a predictor-corrector estimator formulated
based on a stability theorem, used to confine the estimated states
within a neighborhood of their true values A concise tutorial on
deep learning and reinforcement learning A detailed presentation of
the expectation maximization algorithm and its machine
learning-based variants, used for joint state and parameter
estimation Guidelines for constructing nonparametric Bayesian
models from parametric ones Perfect for researchers, professors,
and graduate students in engineering, computer science, applied
mathematics, and artificial intelligence, Nonlinear Filters: Theory
and Applications will also earn a place in the libraries of those
studying or practicing in fields involving pandemic diseases,
cybersecurity, information fusion, augmented reality, autonomous
driving, urban traffic network, navigation and tracking, robotics,
power systems, hybrid technologies, and finance.
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