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Adaptive Learning Methods for Nonlinear System Modeling (Paperback)
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Adaptive Learning Methods for Nonlinear System Modeling (Paperback)
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Adaptive Learning Methods for Nonlinear System Modeling presents
some of the recent advances on adaptive algorithms and machine
learning methods designed for nonlinear system modeling and
identification. Real-life problems always entail a certain degree
of nonlinearity, which makes linear models a non-optimal choice.
This book mainly focuses on those methodologies for nonlinear
modeling that involve any adaptive learning approaches to process
data coming from an unknown nonlinear system. By learning from
available data, such methods aim at estimating the nonlinearity
introduced by the unknown system. In particular, the methods
presented in this book are based on online learning approaches,
which process the data example-by-example and allow to model even
complex nonlinearities, e.g., showing time-varying and dynamic
behaviors. Possible fields of applications of such algorithms
includes distributed sensor networks, wireless communications,
channel identification, predictive maintenance, wind prediction,
network security, vehicular networks, active noise control,
information forensics and security, tracking control in mobile
robots, power systems, and nonlinear modeling in big data, among
many others. This book serves as a crucial resource for
researchers, PhD and post-graduate students working in the areas of
machine learning, signal processing, adaptive filtering, nonlinear
control, system identification, cooperative systems, computational
intelligence. This book may be also of interest to the industry
market and practitioners working with a wide variety of nonlinear
systems.
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