Engineering practice often has to deal with complex systems of
multiple variable and multiple parameter models almost always with
strong non-linear coupling. The conventional analytical
techniques-based approaches for describing and predicting the
behaviour of such systems in many cases are doomed to failure from
the outset, even in the phase of the construction of a more or less
appropriate mathematical model. These approaches normally are too
categorical in the sense that in the name of modelling accuracy
they try to describe all the structural details of the real
physical system to be modelled. This can significantly increase the
intricacy of the model and may result in a enormous computational
burden without achieving considerable improvement of the solution.
The best paradigm exemplifying this situation may be the classic
perturbation theory: the less significant the achievable
correction, the more work has to be invested to obtain it.
A further important component of machine intelligence is a kind
of structural uniformity giving room and possibility to model
arbitrary particular details a priori not specified and unknown.
This idea is similar to the ready-to-wear industry, which
introduced products, which can be slightly modified later on in
contrast to tailor-made creations aiming at maximum accuracy from
the beginning. These subsequent corrections can be carried out by
machines automatically. This learning ability is a key element of
machine intelligence.
The past decade confirmed that the view of typical components of
the present soft computing as fuzzy logic, neural computing,
evolutionary computation and probabilistic reasoning are of
complementary nature and that the best results can be applied by
their combined application.
Today, the two complementary branches of Machine Intelligence,
that is, Artificial Intelligence and Computational Intelligence
serve as the basis of Intelligent Engineering Systems. The huge
number of scientific results published in Journal and conference
proceedings worldwide substantiates this statement. The present
book contains several articles taking different viewpoints in the
field of intelligent systems. "
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