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Neural Approximations for Optimal Control and Decision (Hardcover, 1st ed. 2020)
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Neural Approximations for Optimal Control and Decision (Hardcover, 1st ed. 2020)
Series: Communications and Control Engineering
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Neural Approximations for Optimal Control and Decision provides a
comprehensive methodology for the approximate solution of
functional optimization problems using neural networks and other
nonlinear approximators where the use of traditional optimal
control tools is prohibited by complicating factors like
non-Gaussian noise, strong nonlinearities, large dimension of state
and control vectors, etc. Features of the text include: * a general
functional optimization framework; * thorough illustration of
recent theoretical insights into the approximate solutions of
complex functional optimization problems; * comparison of classical
and neural-network based methods of approximate solution; * bounds
to the errors of approximate solutions; * solution algorithms for
optimal control and decision in deterministic or stochastic
environments with perfect or imperfect state measurements over a
finite or infinite time horizon and with one decision maker or
several; * applications of current interest: routing in
communications networks, traffic control, water resource
management, etc.; and * numerous, numerically detailed examples.
The authors' diverse backgrounds in systems and control theory,
approximation theory, machine learning, and operations research
lend the book a range of expertise and subject matter appealing to
academics and graduate students in any of those disciplines
together with computer science and other areas of engineering.
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