Application of a Hybrid Neural Fuzzy Inference System to Forecast Solar Intensity

Silva, Francisco; Teixeira, Brigida; Teixeira, Nuno Severiano; Pinto, Tiago; Praça, Isabel; Vale, Zita

doi:10.1109/dexa.2016.044

Cited by 5 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The HyFIS model has emerged as a powerful tool for accurate forecasting in diverse fields. For instance, Silva et al (2016) applied the HyFIS model to forecast solar intensity, overcoming the limitations of traditional models. By combining neural networks and fuzzy logic, the HyFIS model improved prediction accuracy, as demonstrated by its outperformance of traditional models in terms of mean absolute error (MAE): 39.32 W/m 2 compared to 46.34 W/m 2 .…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Stock Market Volatility Using MODWT with HyFIS and FS.HGD Models

Alenezy

Ismail

Wadi

et al. 2023

Risks

View full text Add to dashboard Cite

We enhance the precision of predicting daily stock market price volatility using the maximum overlapping discrete wavelet transform (MODWT) spectral model and two learning approaches: the heuristic gradient descent (FS.HGD) and hybrid neural fuzzy inference system (HyFIS). The FS.HGD approach iteratively updates the model’s parameters based on the error function gradient, while the HyFIS approach combines the advantages of neural networks and fuzzy logic systems to create a more robust and accurate learning model. The MODWT uses five mathematical functions to form a discrete wavelet basis. The dataset used includes the daily closing prices of the Tadawul stock market from August 2011 to December 2019. Inputs were selected based on multiple regression, tolerance, and variance inflation factor tests, and the oil price (Loil) and repo rate (Repo) were identified as input variables. The output variable is represented by the logarithm of the Tadawul stock market price (LSCS). MODWT-LA8 (ARIMA(1,1,0) with drift) outperforms other WT functions on the 80% dataset, with an ME of (0.00000532), MAE of (0.003214182), and MAPE of (0.06449683). The addition of WT functions to the FS.HGD and HyFIS models increases their forecasting ability. Based on the reduced RMSE (0.048), MAE (0.038), and MAPE (0.538), the MODWT-LA8-FS.HGD outperforms traditional models in predicting the remaining 20% of datasets.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Predicting Stock Market Volatility Using MODWT with HyFIS and FS.HGD Models

Alenezy

Ismail

Wadi

et al. 2023

Risks

View full text Add to dashboard Cite

show abstract

“…e FIS converts the fuzzy sets into output values. ese paradigms are efficient tools used in several scientific and engineering applications (e.g., forecasting) where these paradigms are developed to handle many environmental variables [50]. e hybrid neural fuzzy inference system (HyFIS), which was proposed by Kim and Kasabov [51], is a paradigm based on the FIS with the advantage of merging both fuzzy concepts with artificial neural networks (ANNs) [52] and, therefore, optimizing the learning process.…”

Section: Layermentioning

confidence: 99%

Using Artificial Intelligence to Predict Customer Satisfaction with E‐Payment Systems during the COVID‐19 Pandemic

Atawneh

Hamadneh

Jaber

et al. 2022

Journal of Mathematics

View full text Add to dashboard Cite

This study explores the impact of electronic payment systems on Saudi Arabia’s customer satisfaction during the COVID-19 pandemic. Descriptive analytical approach of a sample of 1,025 people living in Saudi Arabia was used to answer the study questions and test its hypotheses. Then, a new hybrid fuzzy inference system (HyFIS) is proposed to predict customer satisfaction during COVID-19 pandemic. The proposed system contemplates customer resistance (CR), access to technology (AT), privacy (PV), costs (CT), and speed of efficiency (SE) as the input variables and customer satisfaction (CS) as the output variable. Various statistical tests are utilized to determine the efficiency of input variables in the obtained data. The statistical tests are multicollinearity tests, reliability and validity, ordinal least square (OLS), fixed effect, and random development. As a result, we can determine each input variable’s direct and indirect impact on the CS. Under OLS, fixed effect, and unexpected effect, the SE, CT, PV, AT, and CR considerably impact EP. The EP has been shown to have substantial positive indirect implications. Under OLS, fixed effect, and random effect, the CT, PV, and CR are found to have a significant positive impact on CS. In addition, the AT has a substantial impact on CS in a fixed effect indirect effect. The results of HyFIS were compared to those of the adaptive network-based fuzzy inference system (ANFIS). The results reveal that HyFIS outperforms ANFIS in predicting CS based on the error criterion.

show abstract

“…ANN is able to learn, train, and predict the data to deal with uncertainty. They use a method similar to the mechanism of human brain work through layers of neurons [5]. Since the ANN become one of the most important topics that the science concerns in to improve and adapt machines in every way to serve the human being.…”

Section: Introductionmentioning

confidence: 99%

CO2 Concentration Forecasting in an Office Using Artificial Neural Network

Khorram

Faria

Abrishambaf

et al. 2019

2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)

Self Cite

View full text Add to dashboard Cite

Uncertainty is the state of all operation, components, and objective environment that makes impossible to describe the existing state. Forecasting techniques are essential in the field of knowledge development to overcome the uncertainty to increase the efficiency of all systems. In this paper, artificial neural network algorithm is applied to forecast the CO2 concentration in an office building. The algorithm is implemented in Rstudio software using neural net package. The case study of the paper presents two scenarios with different input data to propose the impacts of train data on forecasting algorithms results. The used dataset in the case study is real data that have been monitored for 2 years. The obtained results of algorithms show the predicted values of CO2 concentration in one office for 600 minutes of a working day. The mean percentage error means absolute percentage error, and standard deviation of predicted data for both scenarios are presented in results section.

show abstract

Application of a Hybrid Neural Fuzzy Inference System to Forecast Solar Intensity

Cited by 5 publications

References 15 publications

Predicting Stock Market Volatility Using MODWT with HyFIS and FS.HGD Models

Predicting Stock Market Volatility Using MODWT with HyFIS and FS.HGD Models

Using Artificial Intelligence to Predict Customer Satisfaction with E‐Payment Systems during the COVID‐19 Pandemic

CO2 Concentration Forecasting in an Office Using Artificial Neural Network

Contact Info

Product

Resources

About