£107.73

Springer Fuzzy Data Analysis - Theory and Decision Library B

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Description

In many real-world scenarios, data is not composed of perfect real numbers. Instead, researchers and professionals often deal with imprecise information such as marks, scores, verbal evaluations, and expert opinions. Fuzzy Data Analysis provides a necessary framework for handling these types of non-numeric datasets effectively. This volume from the Theory and Decision Library B series presents recent results and methods for processing fuzzy data. It offers practical approaches for identifying sub-structures within knowledge bases, which is essential for evaluating functional relationships. These methods are particularly useful when developing diagnostic or control systems that rely on imprecise observations or grey tone pictures. By applying ideas from fuzzy set theory, the authors provide solutions to problems typically addressed by standard data analysis but adapted for the complexities of fuzzy data. This book serves as a technical resource for those looking to improve how they process and interpret qualitative or imprecise information in complex decision-making environments.

Key Features

Provides methods for processing imprecise data like marks, scores, and verbal evaluations.

Offers approaches for finding sub-structures in knowledge bases to evaluate functional relationships.

Applies fuzzy set theory to solve problems usually handled by standard real-number data analysis.

Includes techniques for managing expert opinions and grey tone pictures in data sets.

Supports the development of specialized diagnostic or control systems using fuzzy modeling.

Product Specifications

Format
paperback
Domain
Amazon UK
Publication Date
25 September 2012
Listed Since
07 November 2013

Barcode

No barcode data available

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