In control and signal processing, adaptation is a natural tool to
cope with real-time changes in the dynamical behaviour of signals
and systems. In this area, strongly connected with prediction and
identification, there has been an increasing interest in switching
and supervising methods. Moreover in recent years, special
attention has been paid to the ideas evolving round the theory of
statistical learning as a potential tool of improved adaptation.
The IFAC workshop on Adaptation and Learning in Control and Signal
Processing in 2001 gathered together experts in the field and
interested researchers from universities and industry to present a
full picture of the area. This proceedings volume presents papers
covering the following subjects: Model reference and predictive
control; Multiple model control; Adaptive control I/II; Adaptive
control and learning; Learning; Adaptive control of nonlinear
systems I/II; Supervisory control; Neural networks for control; PID
design methods; Sliding mode; Adaptive filtering and estimation;
Identification methods I/II.
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