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The paradigm of complexity is pervading both science and
engineering, le- ing to the emergence of novel approaches oriented
at the development of a systemic view of the phenomena under study;
the de?nition of powerful tools for modelling, estimation, and
control; and the cross-fertilization of di?erent disciplines and
approaches. One of the most promising paradigms to cope with
complexity is that of networked systems. Complex, dynamical
networks are powerful tools to model, estimate, and control many
interesting phenomena, like agent coordination, synch- nization,
social and economics events, networks of critical infrastructures,
resourcesallocation, informationprocessing,
controlovercommunicationn- works, etc. Advances in this ?eld are
highlighting approaches that are more and more
oftenbasedondynamicalandtime-varyingnetworks,
i.e.networksconsisting of dynamical nodes with links that can
change over time. Moreover, recent technological advances in
wireless communication and decreasing cost and size of electronic
devices are promoting the appearance of large inexpensive
interconnected systems, each with computational, sensing and mobile
ca- bilities. This is fostering the development of many engineering
applications, which exploit the availability of these systems of
systems to monitor and control very large-scale phenomena with ?ne
resoluti
The paradigm of complexity is pervading both science and
engineering, le- ing to the emergence of novel approaches oriented
at the development of a systemic view of the phenomena under study;
the de?nition of powerful tools for modelling, estimation, and
control; and the cross-fertilization of di?erent disciplines and
approaches. One of the most promising paradigms to cope with
complexity is that of networked systems. Complex, dynamical
networks are powerful tools to model, estimate, and control many
interesting phenomena, like agent coordination, synch- nization,
social and economics events, networks of critical infrastructures,
resourcesallocation, informationprocessing,
controlovercommunicationn- works, etc. Advances in this ?eld are
highlighting approaches that are more and more
oftenbasedondynamicalandtime-varyingnetworks,
i.e.networksconsisting of dynamical nodes with links that can
change over time. Moreover, recent technological advances in
wireless communication and decreasing cost and size of electronic
devices are promoting the appearance of large inexpensive
interconnected systems, each with computational, sensing and mobile
ca- bilities. This is fostering the development of many engineering
applications, which exploit the availability of these systems of
systems to monitor and control very large-scale phenomena with ?ne
resoluti
This Festschrift is intended as a homage to our esteemed colleague,
friend and maestro Giorgio Picci on the occasion of his sixty-?fth
birthday. We have knownGiorgiosince our undergraduatestudies at the
University of Padova, wherewe ?rst
experiencedhisfascinatingteachingin theclass ofSystem
Identi?cation. While progressing through the PhD program, then
continuing to collaborate with him and eventually becoming
colleagues, we have had many opportunitiesto appreciate the value
of Giorgio as a professor and a scientist, and chie?y as a person.
We learned a lot from him and we feel indebted for his scienti?c
guidance, his constant support, encouragement and enthusiasm. For
these reasons we are proud to dedicate this book to Giorgio. The
articles in the volume will be presented by prominent researchers
at the "- ternational Conference on Modeling, Estimation and
Control: A Symposium in Honor of Giorgio Picci on the Occasion of
his Sixty-Fifth Birthday," to be held in Venice on October 4-5,
2007. The material covers a broad range of topics in mathematical
systems theory, esti- tion, identi?cation and control, re?ecting
the wide network of scienti?c relationships established during the
last thirty years between the authors and Giorgio. Critical d-
cussion of fundamental concepts, close collaboration on speci?c
topics, joint research programs in this group of talented people
have nourished the development of the ?eld, where Giorgio has
contributed to establishing several cornerstones.
This open access book provides a comprehensive treatment of recent
developments in kernel-based identification that are of interest to
anyone engaged in learning dynamic systems from data. The reader is
led step by step into understanding of a novel paradigm that
leverages the power of machine learning without losing sight of the
system-theoretical principles of black-box identification. The
authors' reformulation of the identification problem in the light
of regularization theory not only offers new insight on classical
questions, but paves the way to new and powerful algorithms for a
variety of linear and nonlinear problems. Regression methods such
as regularization networks and support vector machines are the
basis of techniques that extend the function-estimation problem to
the estimation of dynamic models. Many examples, also from
real-world applications, illustrate the comparative advantages of
the new nonparametric approach with respect to classic parametric
prediction error methods. The challenges it addresses lie at the
intersection of several disciplines so Regularized System
Identification will be of interest to a variety of researchers and
practitioners in the areas of control systems, machine learning,
statistics, and data science.This is an open access book.
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