Linguistic modeling by hierarchical systems of linguistic rules

In this paper, we are going to propose an approach to design linguistic models which are accurate to a high degree and may be suitably interpreted. This approach will be based on the development of a Hierarchical System of Linguistic Rules learning methodology. This methodology has been thought as a...

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Detalles Bibliográficos
Publicado: 2002
Materias:
Genetic algorithms
Hierarchical knowledge base
Hierarchical linguistic partitions
Linguistic modeling
Mamdani-type fuzzy rule-based systems
Rule selection
Knowledge based systems
Learning systems
Linguistics
Fuzzy rule-based systems
Fuzzy sets
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10636706_v10_n1_p2_Cordon
http://hdl.handle.net/20.500.12110/paper_10636706_v10_n1_p2_Cordon
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_10636706_v10_n1_p2_Cordon
record_format dspace
spelling paper:paper_10636706_v10_n1_p2_Cordon2023-06-08T16:04:03Z Linguistic modeling by hierarchical systems of linguistic rules Genetic algorithms Hierarchical knowledge base Hierarchical linguistic partitions Linguistic modeling Mamdani-type fuzzy rule-based systems Rule selection Genetic algorithms Knowledge based systems Learning systems Linguistics Fuzzy rule-based systems Fuzzy sets In this paper, we are going to propose an approach to design linguistic models which are accurate to a high degree and may be suitably interpreted. This approach will be based on the development of a Hierarchical System of Linguistic Rules learning methodology. This methodology has been thought as a refinement of simple linguistic models which, preserving their descriptive power, introduces small changes to increase their accuracy. To do so, we extend the structure of the Knowledge Base of Fuzzy Rule Base Systems in a hierarchical way, in order to make it more flexible. This flexibilization will allow us to have linguistic rules defined over linguistic partitions with different granularity levels, and thus to improve the modeling of those problem subspaces where the former models have bad performance. 2002 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10636706_v10_n1_p2_Cordon http://hdl.handle.net/20.500.12110/paper_10636706_v10_n1_p2_Cordon
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Genetic algorithms
Hierarchical knowledge base
Hierarchical linguistic partitions
Linguistic modeling
Mamdani-type fuzzy rule-based systems
Rule selection
Genetic algorithms
Knowledge based systems
Learning systems
Linguistics
Fuzzy rule-based systems
Fuzzy sets
spellingShingle Genetic algorithms
Hierarchical knowledge base
Hierarchical linguistic partitions
Linguistic modeling
Mamdani-type fuzzy rule-based systems
Rule selection
Genetic algorithms
Knowledge based systems
Learning systems
Linguistics
Fuzzy rule-based systems
Fuzzy sets
Linguistic modeling by hierarchical systems of linguistic rules
topic_facet Genetic algorithms
Hierarchical knowledge base
Hierarchical linguistic partitions
Linguistic modeling
Mamdani-type fuzzy rule-based systems
Rule selection
Genetic algorithms
Knowledge based systems
Learning systems
Linguistics
Fuzzy rule-based systems
Fuzzy sets
description In this paper, we are going to propose an approach to design linguistic models which are accurate to a high degree and may be suitably interpreted. This approach will be based on the development of a Hierarchical System of Linguistic Rules learning methodology. This methodology has been thought as a refinement of simple linguistic models which, preserving their descriptive power, introduces small changes to increase their accuracy. To do so, we extend the structure of the Knowledge Base of Fuzzy Rule Base Systems in a hierarchical way, in order to make it more flexible. This flexibilization will allow us to have linguistic rules defined over linguistic partitions with different granularity levels, and thus to improve the modeling of those problem subspaces where the former models have bad performance.
title Linguistic modeling by hierarchical systems of linguistic rules
title_short Linguistic modeling by hierarchical systems of linguistic rules
title_full Linguistic modeling by hierarchical systems of linguistic rules
title_fullStr Linguistic modeling by hierarchical systems of linguistic rules
title_full_unstemmed Linguistic modeling by hierarchical systems of linguistic rules
title_sort linguistic modeling by hierarchical systems of linguistic rules
publishDate 2002
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10636706_v10_n1_p2_Cordon
http://hdl.handle.net/20.500.12110/paper_10636706_v10_n1_p2_Cordon
_version_ 1768545154451374080

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