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Foundations of Computational Intelligence Volume 2 - Approximate Reasoning (Hardcover, 2009 ed.)
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Foundations of Computational Intelligence Volume 2 - Approximate Reasoning (Hardcover, 2009 ed.)
Series: Studies in Computational Intelligence, 202
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Foundations of Computational Intelligence Volume 2: Approximation
Reasoning: Theoretical Foundations and Applications Human reasoning
usually is very approximate and involves various types of -
certainties. Approximate reasoning is the computational modelling
of any part of the process used by humans to reason about natural
phenomena or to solve real world problems. The scope of this book
includes fuzzy sets, Dempster-Shafer theory, multi-valued logic,
probability, random sets, and rough set, near set and hybrid
intelligent systems. Besides research articles and expository
papers on t- ory and algorithms of approximation reasoning, papers
on numerical experiments and real world applications were also
encouraged. This Volume comprises of 12 chapters including an
overview chapter providing an up-to-date and state-of-the research
on the applications of Computational Intelligence techniques for -
proximation reasoning. The Volume is divided into 2 parts: Part-I:
Approximate Reasoning - Theoretical Foundations Part-II:
Approximate Reasoning - Success Stories and Real World Applications
Part I on Approximate Reasoning - Theoretical Foundations contains
four ch- ters that describe several approaches of fuzzy and Para
consistent annotated logic approximation reasoning. In Chapter 1,
"Fuzzy Sets, Near Sets, and Rough Sets for Your Computational
Intelligence Toolbox" by Peters considers how a user might utilize
fuzzy sets, near sets, and rough sets, taken separately or taken
together in hybridizations as part of a computational intelligence
toolbox. In multi-criteria decision making, it is necessary to
aggregate (combine) utility values corresponding to several
criteria (parameters).
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