Linearity testing for fuzzy rule-based models

José Luis Aznarte; Marcelo C. Medeiros; José M. Benítez

doi:10.1016/j.fss.2010.01.005

Linearity testing for fuzzy rule-based models

José Luis Aznarte
, Marcelo C. Medeiros
, José M. Benítez

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper, we introduce a linearity test for fuzzy rule-based models in the framework of time series modeling. To do so, we explore a family of statistical models, the regime switching autoregressive models, and the relations that link them to the fuzzy rule-based models. From these relations, we derive a Lagrange multiplier linearity test and some properties of the maximum likelihood estimator needed for it. Finally, an empirical study of the goodness of the test is presented.

Original language	English (US)
Pages (from-to)	1836-1851
Number of pages	16
Journal	Fuzzy Sets and Systems
Volume	161
Issue number	13
DOIs	https://doi.org/10.1016/j.fss.2010.01.005
State	Published - Jul 1 2010
Externally published	Yes

Keywords

Fuzzy rule-based models
Linearity test
Statistical inference
Time series

ASJC Scopus subject areas

Logic
Artificial Intelligence

Online availability

10.1016/j.fss.2010.01.005

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title = "Linearity testing for fuzzy rule-based models",

abstract = "In this paper, we introduce a linearity test for fuzzy rule-based models in the framework of time series modeling. To do so, we explore a family of statistical models, the regime switching autoregressive models, and the relations that link them to the fuzzy rule-based models. From these relations, we derive a Lagrange multiplier linearity test and some properties of the maximum likelihood estimator needed for it. Finally, an empirical study of the goodness of the test is presented.",

keywords = "Fuzzy rule-based models, Linearity test, Statistical inference, Time series",

author = "Aznarte, \{Jos{\'e} Luis\} and Medeiros, \{Marcelo C.\} and Ben{\'i}tez, \{Jos{\'e} M.\}",

note = "The research included in this work is part of the first author{\textquoteright}s PhD dissertation at the Department of Computer Science and Artificial Intelligence of the University of Granada. It has been partially supported by the Ministerio de Ciencia e Innovaci{\'o}n of the Spanish Government through Research Grants Ref. TIN2009-14575 and CIT-460000-2009-46.",

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doi = "10.1016/j.fss.2010.01.005",

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AU - Aznarte, José Luis

AU - Medeiros, Marcelo C.

AU - Benítez, José M.

N1 - The research included in this work is part of the first author’s PhD dissertation at the Department of Computer Science and Artificial Intelligence of the University of Granada. It has been partially supported by the Ministerio de Ciencia e Innovación of the Spanish Government through Research Grants Ref. TIN2009-14575 and CIT-460000-2009-46.

PY - 2010/7/1

Y1 - 2010/7/1

N2 - In this paper, we introduce a linearity test for fuzzy rule-based models in the framework of time series modeling. To do so, we explore a family of statistical models, the regime switching autoregressive models, and the relations that link them to the fuzzy rule-based models. From these relations, we derive a Lagrange multiplier linearity test and some properties of the maximum likelihood estimator needed for it. Finally, an empirical study of the goodness of the test is presented.

AB - In this paper, we introduce a linearity test for fuzzy rule-based models in the framework of time series modeling. To do so, we explore a family of statistical models, the regime switching autoregressive models, and the relations that link them to the fuzzy rule-based models. From these relations, we derive a Lagrange multiplier linearity test and some properties of the maximum likelihood estimator needed for it. Finally, an empirical study of the goodness of the test is presented.

KW - Fuzzy rule-based models

KW - Linearity test

KW - Statistical inference

KW - Time series

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M3 - Article

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SP - 1836

EP - 1851

JO - Fuzzy Sets and Systems

JF - Fuzzy Sets and Systems

IS - 13

ER -

Linearity testing for fuzzy rule-based models

Abstract

Keywords

ASJC Scopus subject areas

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