A Hybridized Forecasting Method Based on Weight Adjustment of Neural Network Using Generalized Type-2 Fuzzy Set

Pal, Shanoli Samui; Kar, Samarjit

doi:10.1007/s40815-018-0534-z

Cited by 28 publications

(17 citation statements)

References 28 publications

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“…The existence of supercomputers makes it much easier to compute big data, but researchers need to work hard to implement T2F-NN hardware with the suitable memory, as it can be used in some applications such as aerial or robotic bees, or standalone and self-governing systems, due to lack of access to the supercomputers. Finally, it can be said that if very high accuracy is achieved and time is not a priority, higher-order typ-3 and type-2 FLSs can be used (Baraka and Panoutsos 2019 ; Luo et al 2019 ; Wei et al 2020 ; Lin et al xxxx; Pal and Kar 2019 ).…”

Section: T2f-nnsmentioning

confidence: 99%

A review on type-2 fuzzy neural networks for system identification

2021

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In many engineering problems, the systems dynamics are uncertain, and then, the accurate dynamic modeling is required. Type-2 fuzzy neural networks (T2F-NNs) are extensively used in system identification problems, because of their strong estimation capability. In this paper, the application of T2F-NNs is reviewed and classified. First, an introduction to the principles of system identification, including how to extract data from a system, persistency of excitation, preprocessing of information and data, removal of outlier data, and sorting of data to learn the T2F-NNs, is presented. Then, various learning methods for structure and parameters of the T2F-NNs are reviewed and analyzed. A number of different T2F-NNs that have been used to system identification are reviewed, and their disadvantages and advantages are described. Also, their efficiency in different applications is reviewed. Finally, we will look at the horizon ahead in this issue and analyze its challenges.

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Section: T2f-nnsmentioning

confidence: 99%

A review on type-2 fuzzy neural networks for system identification

2021

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“…YSA-PSO tekniğinin regresyon analizinden daha iyi sonuç verdiğini tespit etmişlerdir. Pal ve Kar [15] yapay sinir ağlarının ağırlıklarını bulanık mantık yöntemini kullanarak optimize etmişlerdir. Geliştirdikleri modeli Mackey-Glass zaman serisi yöntemi ile karşılaştırmışlardır.…”

Section: Li̇teratur Taramasi (Literature Review)unclassified

Jaya algoritması ile optimize edilmiş yapay sinir ağlarını kullanarak Türkiye’de elektrik enerjisi tüketiminin tahmini

Uzlu

Dede

2020

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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In this study, Turkey's electric energy consumption was predicted by using artificial neural networks with Jaya algorithm. As shown in Fig. A, three-layer ANN model encompassing GDP, population, and import-export values as entry parameters was formed with electric energy consumption as the exit parameter. Jaya algorithm was employed for network training. Figure A. Proposed ANN model for electric energy consumptionPurpose: The main objective of the present study was to apply ANN (artificial neural network) model with the Jaya algorithm to estimate electric energy consumption (EEC) in Turkey. Theory and Methods:The model estimates EEC based on gross domestic product (GDP), population, import, and export data. Data from 1980 to 2008 were used to develop the model and data from 2009 to 2014 were used to evaluate the model's predictive ability. ANN-Jaya model's performance was compared with the performance of artificial bee colony (ABC) and teaching learning based optimization (TLBO) algorithm-trained ANN models. After defining the best model according to various error criteria, EEC values for Turkey were predicted under two different scenarios.Results: Simulation results show that, ANN-Jaya modeling performed better results than ANN-TLBO or ANN-ABC modeling. According to the root mean square error values obtained for the test set, the proposed ANN-Jaya model performed 58.9% and 43.9% better than the ANN-ABC and ANN-TLBO models, respectively. Conclusion:In this study, the EEC in Turkey between 1980 and 2023 was modeled based on GDP, population, import, and export indicators under two different scenarios. The two scenarios showed that the future EEC of Turkey would vary between 311.5 and 333.6 TWh in 2023.

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“…The method could be used as a basis for much needed long-term load predictions for European countries. Pal et al [11] proposed a hybridized forecasting model based on weight adjustment of neural networks with BP learning using general type-2 fuzzy sets. Parvez et al [12] proposed a Multilayer Perceptron (MLP)-based photo voltaic forecasting method for the rooftop photovoltaic systems of the smart home.…”

Section: Introductionmentioning

confidence: 99%

An Ultra-Short-Term Electrical Load Forecasting Method Based on Temperature-Factor-Weight and LSTM Model

Zhang

Tong

et al. 2020

Energies

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Ultra-short-term electrical load forecasting is an important guarantee for the safety and efficiency of energy system operation. Temperature is also an important factor affecting the changes in electric load. However, in different cases, the impact of temperature on load forecasting will vary greatly, and sometimes even lead to the decrease of forecasting accuracy. This often brings great difficulties to researchers’ work. In order to make more scientific use of temperature factor for ultra-short-term electrical load forecasting, especially to avoid the negative influence of temperature on load forecasting, in this paper we propose an ultra-short-term electrical load forecasting method based on temperature factor weight and long short-term memory model. The proposed method evaluates the importance of the current prediction task’s temperature based on the change magnitude of the recent load and the correlation between temperature and load, and therefore the negative impacts of the temperature model can be avoided. The mean absolute percentage error of proposed method is decreased by 1.24%, 1.86%, and 6.21% compared with traditional long short-term memory model, back-propagation neural network, and gray model on average, respectively. The experimental results demonstrate that this method has obvious advantages in prediction accuracy and generalization ability.

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A Hybridized Forecasting Method Based on Weight Adjustment of Neural Network Using Generalized Type-2 Fuzzy Set

Cited by 28 publications

References 28 publications

A review on type-2 fuzzy neural networks for system identification

A review on type-2 fuzzy neural networks for system identification

Jaya algoritması ile optimize edilmiş yapay sinir ağlarını kullanarak Türkiye’de elektrik enerjisi tüketiminin tahmini

An Ultra-Short-Term Electrical Load Forecasting Method Based on Temperature-Factor-Weight and LSTM Model

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