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? J. Andersen Niels Bohr Institute for Astronomy Physics and
Geophysics Astronomical Observatory Copenhagen [email protected] The
development of astronomy worldwide begins at the roots: Already
from childhood, humans of all nations and civilizations seem to
share an innate fascination with the sky. Yet, people in different
regions of the world have vastly different possibilities for
pursuing this interest. In wealthy, industrialised societies the
way is open to a school or higher education in science, possibly
leading to a career in astronomy or basic or applied space science
for the benefit of the country as well as the individual. In other
regions, neither the financial nor the trained human resources are
sufficient to offer that avenue to the future of the young
generation, or those intellectual resources to the development of
their country. This book addresses ways and means by which these
obstacles can be, if not fully overcome, then at least
significantly reduced.
The papers in this volume present theoretical aspects and applications of artificial neural networks and genetic algorithms. Also included are papers on fuzzy logic, soft computing, and artificial intelligence. Fundamental issues are addressed such as the nonlinear approximation capabilities of neural networks and formal methods of data representation with topological properties. New elements in genetic algorithms are presented, for example, crossover methods and gene representation. Papers on applications of neural networks show how successful these methods are in a wide range of fields like meteorological and atmospheric pollution forecasts, furnace control, and system identification. Genetic algorithms are used to solve optimization problems related to shipping and computer vision. Fuzzy-logic-based techniques are applied to sociodynamic models and hybrid neuro-fuzzy models.
There is hardly a science that is without the notion of "system."
We have systems in mathematics, formal systems in logic, systems in
physics, electrical and mechanical engineering, architectural-,
operating-, infonnation-, programming systems in computer science,
management-and PJoduction systems in industrial applications,
economical-, ecological-, biological systems, and many more. In
many of these disciplines formal tools for system specification,
construction, verification, have been developed as well as
mathematical concepts for system modeling and system simulation.
Thus it is quite natural to expect that systems theory as an
interdisciplinary and well established science offering general
concepts and methods for a wide variety of applications is a
subject in its own right in academic education. However, as can be
seen from the literature and from the curricula of university
studies -at least in Central Europe-, it is subordinated and either
seen as part of mathematics with the risk that mathematicians, who
may not be familiar with applications, define it in their own way,
or it is treated separately within each application field focusing
on only those aspects which are thought to be needed in the
particular application. This often results in uneconomical
re-inventing and re-naming of concepts and methods within one
field, while the same concepts and methods are already well
introduced and practiced in other fields. The fundamentals on
general systems theory were developed several decades ago. We note
the pioneering work of M. A. Arbib, R. E. Kalman, G. 1. Klir, M. D.
This book presents a collection of revised refereed papers selected
from the contributions to the Fifth International Workshop on
Computer Aided Systems Theory, EUROCAST '95, held in Innsbruck,
Austria in May 1995.
The 42 full papers contained have been contributed by CAST
theoreticians, tool-makers, designers, and appliers and reflect the
full spectrum of activities in the area. The papers are organized
in sections on systems theory, design environments, complex systems
design, and specific applications.
One ofthe most important aspects in research fields where
mathematics is "applied is the construction of a formal model of a
real system. As for structural relations, graphs have turned out to
provide the most appropriate tool for setting up the mathematical
model. This is certainly one of the reasons for the rapid expansion
in graph theory during the last decades. Furthermore, in recent
years it also became clear that the two disciplines of graph theory
and computer science have very much in common, and that each one
has been capable of assisting significantly in the development of
the other. On one hand, graph theorists have found that many of
their problems can be solved by the use of com puting techniques,
and on the other hand, computer scientists have realized that many
of their concepts, with which they have to deal, may be
conveniently expressed in the lan guage of graph theory, and that
standard results in graph theory are often very relevant to the
solution of problems concerning them. As a consequence, a
tremendous number of publications has appeared, dealing with
graphtheoretical problems from a computational point of view or
treating computational problems using graph theoretical concepts."
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