Adaptive systems are widely encountered in many applications ranging
through adaptive filtering and more generally adaptive signal
processing, systems identification and adaptive control, to pattern
recognition and machine intelligence: adaptation is now recognised as
keystone of "intelligence" within computerised systems. These diverse
areas echo the classes of models which conveniently describe each
corresponding system. Thus although there can hardly be a "general
theory of adaptive systems" encompassing both the modelling task and the
design of the adaptation procedure, nevertheless, these diverse issues
have a major common component: namely the use of adaptive algorithms,
also known as stochastic approximations in the mathematical statistics
literature, that is to say the adaptation procedure (once all modelling
problems have been resolved). The juxtaposition of these two expressions
in the title reflects the ambition of the authors to produce a reference
work, both for engineers who use these adaptive algorithms and for
probabilists or statisticians who would like to study stochastic
approximations in terms of problems arising from real applications.
Hence the book is organised in two parts, the first one user-oriented,
and the second providing the mathematical foundations to support the
practice described in the first part. The book covers the topcis of
convergence, convergence rate, permanent adaptation and tracking, change
detection, and is illustrated by various realistic applications
originating from these areas of applications.