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Intuitionistic time series fuzzy inference system

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Intuitionistic time series fuzzy inference system. / Egrioglu, Erol; Bas, Eren; Yolcu, Ozge Cagcag; Yolcu, Ufuk.

In: Engineering Applications of Artificial Intelligence, Vol. 82, 30.06.2019, p. 175-183.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Egrioglu, E, Bas, E, Yolcu, OC & Yolcu, U 2019, 'Intuitionistic time series fuzzy inference system', Engineering Applications of Artificial Intelligence, vol. 82, pp. 175-183. https://doi.org/10.1016/j.engappai.2019.03.024

APA

Egrioglu, E., Bas, E., Yolcu, O. C., & Yolcu, U. (2019). Intuitionistic time series fuzzy inference system. Engineering Applications of Artificial Intelligence, 82, 175-183. https://doi.org/10.1016/j.engappai.2019.03.024

Vancouver

Egrioglu E, Bas E, Yolcu OC, Yolcu U. Intuitionistic time series fuzzy inference system. Engineering Applications of Artificial Intelligence. 2019 Jun 30;82:175-183. https://doi.org/10.1016/j.engappai.2019.03.024

Author

Egrioglu, Erol ; Bas, Eren ; Yolcu, Ozge Cagcag ; Yolcu, Ufuk. / Intuitionistic time series fuzzy inference system. In: Engineering Applications of Artificial Intelligence. 2019 ; Vol. 82. pp. 175-183.

Bibtex

@article{e62f3fb5bab04f928e7a69e043f877ab,
title = "Intuitionistic time series fuzzy inference system",
abstract = "Although adaptive network fuzzy inference system and fuzzy functions approach can be utilized as a prediction tool, they have been not designed for prediction problem and they ignore the dependency structure of time series observations. From this viewpoint, making a design of the method that considers the dependency structure of observations will provide to get more accurate prediction. In this study, an intuitionistic time series fuzzy inference system (I-TSFIS) has been proposed. In the I-TSFIS, in just the same way as in the intuitionistic fuzzy inference systems, not only the membership values and crisp observations but also the non-membership values are used as inputs. Moreover, due to the use of crisp values as targets and outputs, the output does not need to be deffuzzified. Non-linear relationships between inputs and outputs of the proposed I-TSFIS are determined by Sigma-Pi neural network (SP-NN). The obtaining of optimal weights of SP-NN is performed by modified particle swarm optimization. And also I-TSFIS uses intuitionistic fuzzy C-means to obtain fuzzy clusters, membership and non-membership values of observations for these clusters. To evaluate the prediction performance of the proposed I-TSFIS, various real-life time series data sets have been analyzed and the results demonstrate the superior prediction ability of the proposed I-TSFIS.",
keywords = "Fuzzy inference system, Intuitionistic fuzzy c-means, Particle swarm optimization, Sigma-pi neural network, Time series prediction",
author = "Erol Egrioglu and Eren Bas and Yolcu, {Ozge Cagcag} and Ufuk Yolcu",
year = "2019",
month = jun,
day = "30",
doi = "10.1016/j.engappai.2019.03.024",
language = "English",
volume = "82",
pages = "175--183",
journal = "Engineering Applications of Artificial Intelligence",
issn = "0952-1976",
publisher = "Elsevier Limited",

}

RIS

TY - JOUR

T1 - Intuitionistic time series fuzzy inference system

AU - Egrioglu, Erol

AU - Bas, Eren

AU - Yolcu, Ozge Cagcag

AU - Yolcu, Ufuk

PY - 2019/6/30

Y1 - 2019/6/30

N2 - Although adaptive network fuzzy inference system and fuzzy functions approach can be utilized as a prediction tool, they have been not designed for prediction problem and they ignore the dependency structure of time series observations. From this viewpoint, making a design of the method that considers the dependency structure of observations will provide to get more accurate prediction. In this study, an intuitionistic time series fuzzy inference system (I-TSFIS) has been proposed. In the I-TSFIS, in just the same way as in the intuitionistic fuzzy inference systems, not only the membership values and crisp observations but also the non-membership values are used as inputs. Moreover, due to the use of crisp values as targets and outputs, the output does not need to be deffuzzified. Non-linear relationships between inputs and outputs of the proposed I-TSFIS are determined by Sigma-Pi neural network (SP-NN). The obtaining of optimal weights of SP-NN is performed by modified particle swarm optimization. And also I-TSFIS uses intuitionistic fuzzy C-means to obtain fuzzy clusters, membership and non-membership values of observations for these clusters. To evaluate the prediction performance of the proposed I-TSFIS, various real-life time series data sets have been analyzed and the results demonstrate the superior prediction ability of the proposed I-TSFIS.

AB - Although adaptive network fuzzy inference system and fuzzy functions approach can be utilized as a prediction tool, they have been not designed for prediction problem and they ignore the dependency structure of time series observations. From this viewpoint, making a design of the method that considers the dependency structure of observations will provide to get more accurate prediction. In this study, an intuitionistic time series fuzzy inference system (I-TSFIS) has been proposed. In the I-TSFIS, in just the same way as in the intuitionistic fuzzy inference systems, not only the membership values and crisp observations but also the non-membership values are used as inputs. Moreover, due to the use of crisp values as targets and outputs, the output does not need to be deffuzzified. Non-linear relationships between inputs and outputs of the proposed I-TSFIS are determined by Sigma-Pi neural network (SP-NN). The obtaining of optimal weights of SP-NN is performed by modified particle swarm optimization. And also I-TSFIS uses intuitionistic fuzzy C-means to obtain fuzzy clusters, membership and non-membership values of observations for these clusters. To evaluate the prediction performance of the proposed I-TSFIS, various real-life time series data sets have been analyzed and the results demonstrate the superior prediction ability of the proposed I-TSFIS.

KW - Fuzzy inference system

KW - Intuitionistic fuzzy c-means

KW - Particle swarm optimization

KW - Sigma-pi neural network

KW - Time series prediction

U2 - 10.1016/j.engappai.2019.03.024

DO - 10.1016/j.engappai.2019.03.024

M3 - Journal article

AN - SCOPUS:85064088515

VL - 82

SP - 175

EP - 183

JO - Engineering Applications of Artificial Intelligence

JF - Engineering Applications of Artificial Intelligence

SN - 0952-1976

ER -