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In the context of this book, adaptation is taken to mean a feature
of a system aimed at achieving the best possible performance, when
mathematical models of the environment and the system itself are
not fully available. This has applications ranging from theories of
visual perception and the processing of information, to the more
technical problems of friction compensation and adaptive
classification of signals in fixed-weight recurrent neural
networks. Largely devoted to the problems of adaptive regulation,
tracking and identification, this book presents a unifying
system-theoretic view on the problem of adaptation in dynamical
systems. Special attention is given to systems with nonlinearly
parameterized models of uncertainty. Concepts, methods and
algorithms given in the text can be successfully employed in wider
areas of science and technology. The detailed examples and
background information make this book suitable for a wide range of
researchers and graduates in cybernetics, mathematical modelling
and neuroscience.
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