Granularity and Specificity in Fuzzy Rule-Based Systems

Thomas Sudkamp

doi:10.1007/978-3-7908-1823-9_11

Granularity and Specificity in Fuzzy Rule-Based Systems

Thomas Sudkamp

Wright State University

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

The structure of fuzzy models produced by a heursitic analysis of the problem domain is compared with that of models algorithmically generated from training data. The trade-offs between granularity, specificity, interpretability, and efficiency are examined for rule-bases produced in each of these manners. An algorithm that combines rule learning with region merging is introduced to incorporate beneficial features of both the heuristic and learning approaches to producing fuzzy models.

Original language	American English
Title of host publication	Granular Computing. Studies in Fuzziness and Soft Computing
DOIs	https://doi.org/10.1007/978-3-7908-1823-9_11
State	Published - Jan 1 2001

Disciplines

Computer Sciences
Engineering
Mathematics
Physical Sciences and Mathematics

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https://link.springer.com/chapter/10.1007/978-3-7908-1823-9_11

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M3 - Chapter

BT - Granular Computing. Studies in Fuzziness and Soft Computing

ER -

Granularity and Specificity in Fuzzy Rule-Based Systems

Abstract

Disciplines

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