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Soft Numerical Computing in Uncertain Dynamic Systems (Paperback)
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Soft Numerical Computing in Uncertain Dynamic Systems (Paperback)
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Soft Numerical Computing in Uncertain Dynamic Systems is intended
for system specialists interested in dynamic systems that operate
at different time scales. The book discusses several types of
errors and their propagation, covering numerical methods-including
convergence and consistence properties and characteristics-and
proving of related theorems within the setting of soft computing.
Several types of uncertainty representation like interval, fuzzy,
type 2 fuzzy, granular, and combined uncertain sets are discussed
in detail. The book can be used by engineering students in control
and finite element fields, as well as all engineering, applied
mathematics, economics, and computer science students. One of the
important topics in applied science is dynamic systems and their
applications. The authors develop these models and deliver
solutions with the aid of numerical methods. Since they are
inherently uncertain, soft computations are of high relevance here.
This is the reason behind investigating soft numerical computing in
dynamic systems. If these systems are involved with
complex-uncertain data, they will be more practical and important.
Real-life problems work with this type of data and most of them
cannot be solved exactly and easily-sometimes they are impossible
to solve. Clearly, all the numerical methods need to consider error
of approximation. Other important applied topics involving
uncertain dynamic systems include image processing and pattern
recognition, which can benefit from uncertain dynamic systems as
well. In fact, the main objective is to determine the coefficients
of a matrix that acts as the frame in the image. One of the
effective methods exhibiting high accuracy is to use finite
differences to fill the cells of the matrix.
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