Modeling and linguistic knowledge extraction from systems using fuzzy relational models

Campello, R.J.G.B., and Amaral, W.C. (2001) Modeling and linguistic knowledge extraction from systems using fuzzy relational models. Fuzzy Sets and Systems, 121 (1). pp. 113-126.

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Abstract

Fuzzy relational models have been widely investigated and found to be an efficient tool for the identification of complex systems. However, little attention has been given to the linguistic interpretation of these models. The use of relational models is recommended since their development follows a natural sequence based on the original ideas about fuzzy sets and fuzzy logic, involving the estimation of the relations existing between linguistic terms which have previously been defined by the user. In the present paper the problem of extracting linguistic knowledge from systems by using relational models is addressed. A new algorithm for the identification of these models which can provide analytical or numerical solutions depending on user requirements is also proposed. Examples are presented showing that both quantitative and qualitative modeling can be effectively achieved by combining the proposed methodologies for identification and extraction of linguistic knowledge from systems.

Item ID: 47661
Item Type: Article (Research - C1)
ISSN: 1872-6801
Keywords: fuzzy modeling, knowledge extraction, linguistic models, relational models
Funders: São Paulo Research Foundation (FAPESP), Brazilian National Research Council (CNPq)
Projects and Grants: FAPESP fellowship 99/03902-6, CNPq fellowship 301345184
Date Deposited: 23 Mar 2017 01:11
FoR Codes: 01 MATHEMATICAL SCIENCES > 0102 Applied Mathematics > 010299 Applied Mathematics not elsewhere classified @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
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