|
Showing 1 - 2 of
2 matches in All Departments
This book provides tools and algorithms for solving a wide class of
optimization tasks by learning from their repetitions. A unified
framework is provided for learning algorithms that are based on the
stochastic gradient (a golden standard in learning), including
random simultaneous perturbations and the response surface the
methodology. Original algorithms include model-free learning of
short decision sequences as well as long sequences-relying on
model-supported gradient estimation. Learning is based on whole
sequences of a process observation that are either vectors or
images. This methodology is applicable to repetitive processes,
covering a wide range from (additive) manufacturing to decision
making for COVID-19 waves mitigation. A distinctive feature of the
algorithms is learning between repetitions-this idea extends the
paradigms of iterative learning and run-to-run control. The main
ideas can be extended to other decision learning tasks, not
included in this book. The text is written in a comprehensible way
with the emphasis on a user-friendly presentation of the
algorithms, their explanations, and recommendations on how to
select them. The book is expected to be of interest to researchers,
Ph.D., and graduate students in computer science and engineering,
operations research, decision making, and those working on the
iterative learning control.
This book provides tools and algorithms for solving a wide class of
optimization tasks by learning from their repetitions. A unified
framework is provided for learning algorithms that are based on the
stochastic gradient (a golden standard in learning), including
random simultaneous perturbations and the response surface the
methodology. Original algorithms include model-free learning of
short decision sequences as well as long sequences-relying on
model-supported gradient estimation. Learning is based on whole
sequences of a process observation that are either vectors or
images. This methodology is applicable to repetitive processes,
covering a wide range from (additive) manufacturing to decision
making for COVID-19 waves mitigation. A distinctive feature of the
algorithms is learning between repetitions-this idea extends the
paradigms of iterative learning and run-to-run control. The main
ideas can be extended to other decision learning tasks, not
included in this book. The text is written in a comprehensible way
with the emphasis on a user-friendly presentation of the
algorithms, their explanations, and recommendations on how to
select them. The book is expected to be of interest to researchers,
Ph.D., and graduate students in computer science and engineering,
operations research, decision making, and those working on the
iterative learning control.
|
|
Email address subscribed successfully.
A activation email has been sent to you.
Please click the link in that email to activate your subscription.