Dynamic fuzzy pattern recognition with applications to finance and engineering by Larisa Angstenberger

Cover of: Dynamic fuzzy pattern recognition with applications to finance and engineering | Larisa Angstenberger

Published by Kluwer Academic in Boston .

Written in English

Read online

Subjects:

  • Pattern perception.,
  • Fuzzy systems.

Edition Notes

Includes bibliographical references (p. [269]-277) and index.

Book details

StatementLarisa Angstenberger.
SeriesInternational series in intelligent technologies -- 17
Classifications
LC ClassificationsQ327 .A57 2001
The Physical Object
Paginationxxii, 287 p. :
Number of Pages287
ID Numbers
Open LibraryOL20644025M
ISBN 100792375033
LC Control Number2001038476

Download Dynamic fuzzy pattern recognition with applications to finance and engineering

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This book sets a general framework for Dynamic Pattern Recognition, describing in detail the monitoring process using fuzzy tools Cited by: Dynamic Fuzzy Pattern Recognition with Applications to Finance and Engineering focuses on fuzzy clustering methods which have proven to be very powerful in pattern recognition and considers the entire process of dynamic pattern recognition.

This book sets a general framework for Dynamic Pattern Recognition, describing in detail the monitoring process using fuzzy tools. Dynamic Fuzzy Pattern Recognition with Applications to Finance and Engineering focuses on fuzzy clustering methods which have proven to be very powerful in pattern recognition and considers the entire process of dynamic pattern recognition.

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[Larisa Angstenberger] -- Dynamic Fuzzy Pattern Recognition with Applications to Finance and Engineering focuses on fuzzy clustering methods which have proven to be very powerful in pattern recognition and considers the. Angstenberger L.

() Applications of Dynamic Pattern Recognition Methods. In: Dynamic Fuzzy Pattern Recognition with Applications to Finance and Engineering. International Series in Intelligent Technologies, vol Author: Larisa Angstenberger.

Fuzzy Models and Algorithms for Pattern Recognition and Image Processing presents a comprehensive introduction of the use of fuzzy models in pattern recognition and selected topics in image processing and computer vision. Unique to this volume in the Kluwer Handbooks of Fuzzy Sets Series is the fact that this book was written in its entirety by its four authors.

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A1jis a one of the fuzzy set of the fuzzy partition for x1 A2kis a one of the fuzzy set of the fuzzy partition for x2 Om i is a one of the fuzzy set of the fuzzy partition for y For a given pair of crisp input values x1 and x2 the antecedents are the degrees of membership obtained during the fuzzification: µA1 j(x1) and µA2 k(x2).

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