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High order fuzzy time series method based on pi-sigma neural network

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High order fuzzy time series method based on pi-sigma neural network. / Bas, Eren; Grosan, Crina; Egrioglu, Erol et al.

In: Engineering Applications of Artificial Intelligence, Vol. 72, 01.06.2018, p. 350-356.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Bas, E, Grosan, C, Egrioglu, E & Yolcu, U 2018, 'High order fuzzy time series method based on pi-sigma neural network', Engineering Applications of Artificial Intelligence, vol. 72, pp. 350-356. https://doi.org/10.1016/j.engappai.2018.04.017

APA

Bas, E., Grosan, C., Egrioglu, E., & Yolcu, U. (2018). High order fuzzy time series method based on pi-sigma neural network. Engineering Applications of Artificial Intelligence, 72, 350-356. https://doi.org/10.1016/j.engappai.2018.04.017

Vancouver

Bas E, Grosan C, Egrioglu E, Yolcu U. High order fuzzy time series method based on pi-sigma neural network. Engineering Applications of Artificial Intelligence. 2018 Jun 1;72:350-356. doi: 10.1016/j.engappai.2018.04.017

Author

Bas, Eren ; Grosan, Crina ; Egrioglu, Erol et al. / High order fuzzy time series method based on pi-sigma neural network. In: Engineering Applications of Artificial Intelligence. 2018 ; Vol. 72. pp. 350-356.

Bibtex

@article{a400e79e91964bf29582d45a41e98e29,
title = "High order fuzzy time series method based on pi-sigma neural network",
abstract = "Fuzzy time series methods, which do not require the strict assumptions of classical time series methods, generally consist of three stages as fuzzification of crisp time series observations, determination of fuzzy relationships and defuzzification. All of these stages play a very important role on the forecasting performance of the model. An important stage of the fuzzy time series analysis is to determine the fuzzy relationships. Artificial neural networks seem to be very effective in determining fuzzy relationships that improve the accuracy of the forecasting performance. Several neuron models with different characteristics have been proposed so far. One of these models is Pi-Sigma neural network. An important advantage of Pi-Sigma neural network is that it requires fewer weights and nodes and has a lower number of computations when compared to multilayer perceptron. In this study, a new model for determining the fuzzy relationships for high order fuzzy time series forecasting which uses Pi-Sigma neural network is introduced. A modified particle swarm optimization model is used to train the Pi-Sigma network. We test the new model on two real datasets and we also perform a simulation study. The results are compared to the ones obtained by other techniques and show a better performance.",
keywords = "Fuzzy relations, Fuzzy time series, Particle swarm optimization, Pi-sigma neural network",
author = "Eren Bas and Crina Grosan and Erol Egrioglu and Ufuk Yolcu",
year = "2018",
month = jun,
day = "1",
doi = "10.1016/j.engappai.2018.04.017",
language = "English",
volume = "72",
pages = "350--356",
journal = "Engineering Applications of Artificial Intelligence",
issn = "0952-1976",
publisher = "Elsevier Limited",

}

RIS

TY - JOUR

T1 - High order fuzzy time series method based on pi-sigma neural network

AU - Bas, Eren

AU - Grosan, Crina

AU - Egrioglu, Erol

AU - Yolcu, Ufuk

PY - 2018/6/1

Y1 - 2018/6/1

N2 - Fuzzy time series methods, which do not require the strict assumptions of classical time series methods, generally consist of three stages as fuzzification of crisp time series observations, determination of fuzzy relationships and defuzzification. All of these stages play a very important role on the forecasting performance of the model. An important stage of the fuzzy time series analysis is to determine the fuzzy relationships. Artificial neural networks seem to be very effective in determining fuzzy relationships that improve the accuracy of the forecasting performance. Several neuron models with different characteristics have been proposed so far. One of these models is Pi-Sigma neural network. An important advantage of Pi-Sigma neural network is that it requires fewer weights and nodes and has a lower number of computations when compared to multilayer perceptron. In this study, a new model for determining the fuzzy relationships for high order fuzzy time series forecasting which uses Pi-Sigma neural network is introduced. A modified particle swarm optimization model is used to train the Pi-Sigma network. We test the new model on two real datasets and we also perform a simulation study. The results are compared to the ones obtained by other techniques and show a better performance.

AB - Fuzzy time series methods, which do not require the strict assumptions of classical time series methods, generally consist of three stages as fuzzification of crisp time series observations, determination of fuzzy relationships and defuzzification. All of these stages play a very important role on the forecasting performance of the model. An important stage of the fuzzy time series analysis is to determine the fuzzy relationships. Artificial neural networks seem to be very effective in determining fuzzy relationships that improve the accuracy of the forecasting performance. Several neuron models with different characteristics have been proposed so far. One of these models is Pi-Sigma neural network. An important advantage of Pi-Sigma neural network is that it requires fewer weights and nodes and has a lower number of computations when compared to multilayer perceptron. In this study, a new model for determining the fuzzy relationships for high order fuzzy time series forecasting which uses Pi-Sigma neural network is introduced. A modified particle swarm optimization model is used to train the Pi-Sigma network. We test the new model on two real datasets and we also perform a simulation study. The results are compared to the ones obtained by other techniques and show a better performance.

KW - Fuzzy relations

KW - Fuzzy time series

KW - Particle swarm optimization

KW - Pi-sigma neural network

U2 - 10.1016/j.engappai.2018.04.017

DO - 10.1016/j.engappai.2018.04.017

M3 - Journal article

AN - SCOPUS:85046335336

VL - 72

SP - 350

EP - 356

JO - Engineering Applications of Artificial Intelligence

JF - Engineering Applications of Artificial Intelligence

SN - 0952-1976

ER -