Nonlinear autoregressive model based on fuzzy relation

Norikazu Ikoma; Kaoru Hirota

doi:10.1016/0020-0255(93)90068-W

Nonlinear autoregressive model based on fuzzy relation

Norikazu Ikoma^*, Kaoru Hirota

^*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

A nonlinear autoregressive model that has a regressive function approximated by fuzzy relations is proposed. The internal parameters of the fuzzy relational matrix describe the characteristics of this function. The estimation method of these parameters is shown from the viewpoint of maximum likelihood estimation. As a result of the numerical experiment on artificial data compared with the ordinary autoregressive model and the polynomial autoregressive model, it is shown that the proposed model is able to estimate the more precise regressive function from the observed data.

Original language	English
Pages (from-to)	131-144
Number of pages	14
Journal	Information Sciences
Volume	71
Issue number	1-2
DOIs	https://doi.org/10.1016/0020-0255(93)90068-W
Publication status	Published - 15 Jun 1993
Externally published	Yes

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Ikoma, N., & Hirota, K. (1993). Nonlinear autoregressive model based on fuzzy relation. Information Sciences, 71(1-2), 131-144. https://doi.org/10.1016/0020-0255(93)90068-W

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AB - A nonlinear autoregressive model that has a regressive function approximated by fuzzy relations is proposed. The internal parameters of the fuzzy relational matrix describe the characteristics of this function. The estimation method of these parameters is shown from the viewpoint of maximum likelihood estimation. As a result of the numerical experiment on artificial data compared with the ordinary autoregressive model and the polynomial autoregressive model, it is shown that the proposed model is able to estimate the more precise regressive function from the observed data.

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DO - 10.1016/0020-0255(93)90068-W

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SN - 0020-0255

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EP - 144

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Nonlinear autoregressive model based on fuzzy relation

Abstract

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