scholarly journals Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 484 ◽  
Author(s):  
Stéfano Frizzo Stefenon ◽  
Roberto Zanetti Freire ◽  
Leandro dos Santos Coelho ◽  
Luiz Henrique Meyer ◽  
Rafael Bartnik Grebogi ◽  
...  
Keyword(s):  
Time Series ◽  
Fuzzy System ◽  
Time Series Forecasting ◽  
Subtractive Clustering ◽  
Anfis Model ◽  
Data Set ◽  
Inference System ◽  
Electrical Insulator ◽  
Fuzzy C Means ◽  
Neuro Fuzzy

The surface contamination of electrical insulators can increase the electrical conductivity of these components, which may lead to faults in the electrical power system. During inspections, ultrasound equipment is employed to detect defective insulators or those that may cause failures within a certain period. Assuming that the signal collected by the ultrasound device can be processed and used for both the detection of defective insulators and prediction of failures, this study starts by presenting an experimental procedure considering a contaminated insulator removed from the distribution line for data acquisition. Based on the obtained data set, an offline time series forecasting approach with an Adaptive Neuro-Fuzzy Inference System (ANFIS) was conducted. To improve the time series forecasting performance and to reduce the noise, Wavelet Packets Transform (WPT) was associated to the ANFIS model. Once the ANFIS model associated with WPT has distinct parameters to be adjusted, a complete evaluation concerning different model configurations was conducted. In this case, three inference system structures were evaluated: grid partition, fuzzy c-means clustering, and subtractive clustering. A performance analysis focusing on computational effort and the coefficient of determination provided additional parameter configurations for the model. Taking into account both parametrical and statistical analysis, the Wavelet Neuro-Fuzzy System with fuzzy c-means showed that it is possible to achieve impressive accuracy, even when compared to classical approaches, in the prediction of electrical insulators conditions.

A hybrid neuro-fuzzy inference system-based algorithm for time series forecasting applied to energy consumption prediction

2020 ◽  
Vol 268 ◽  
pp. 114977 ◽  
Author(s):  
Mohammed Ali Jallal ◽  
Aurora González-Vidal ◽  
Antonio F. Skarmeta ◽  
Samira Chabaa ◽  
Abdelouhab Zeroual
Keyword(s):  
Time Series ◽  
Energy Consumption ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Time Series Forecasting ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

scholarly journals Towards a Neuro-Fuzzy System for Time Series Forecasting in Maintenance Applications

2008 ◽  
Vol 41 (2) ◽  
pp. 12855-12860 ◽  
Author(s):  
M. El-Koujok ◽  
R. Gouriveau ◽  
N. Zerhouni
Keyword(s):  
Time Series ◽  
Fuzzy System ◽  
Time Series Forecasting ◽  
Neuro Fuzzy

Tourism Demand Forecasting Based on a Neuro-Fuzzy Model

2014 ◽  
Vol 1 (1) ◽  
pp. 60-69 ◽  
Author(s):  
George Atsalakis ◽  
Eleni Chnarogiannaki ◽  
Consantinos Zopounidis
Keyword(s):  
Time Series ◽  
Fuzzy Inference ◽  
Demand Forecasting ◽  
Fuzzy Model ◽  
Time Series Models ◽  
Tourism Demand ◽  
Anfis Model ◽  
Inference System ◽  
Forecasting Models ◽  
Neuro Fuzzy

Tourism in Greece plays a major role in the country's economy and an accurate forecasting model for tourism demand is a useful tool, which could affect decision making and planning for the future. This paper answers some questions such as: how did the forecasting techniques evolve over the years, how precise can they be, and in what way can they be used in assessing the demand for tourism? An Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used in making the forecasts. The data used as input for the forecasting models relates to monthly time-series tourist arrivals by air, train, sea and road into Greece from January 1996 until September 2011. 80% of the data has been used to train the forecasting models and the rest to evaluate the models. The performance of the model is achieved by the calculation of some well known statistical errors. The accuracy of the ANFIS model is further compared with two conventional forecasting models: the autoregressive (AR) and autoregressive moving average (ARMA) time-series models. The results were satisfactory even if the collected data were not pleasing enough. The ANFIS performed further compared to the other time-series models. In conclusion, the accuracy of the ANFIS model forecast proved its great importance in tourism demand forecasting.

scholarly journals Performance Comparison of ANFIS Models by Input Space Partitioning Methods

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 700 ◽  
Author(s):  
Chan-Uk Yeom ◽  
Keun-Chang Kwak
Keyword(s):  
Performance Comparison ◽  
Combined Cycle ◽  
Subtractive Clustering ◽  
Segmentation Method ◽  
Clustering Method ◽  
Space Partitioning ◽  
Anfis Model ◽  
Inference System ◽  
Input Space ◽  
Fuzzy C Means

In this paper, we compare the predictive performance of the adaptive neuro-fuzzy inference system (ANFIS) models according to the input space segmentation method. The ANFIS model can be divided into four types according to the method of dividing the input space. In general, the ANFIS1 model using grid partitioning method, ANFIS2 model using subtractive clustering (SC) method, and the ANFIS3 model using fuzzy C-means (FCM) clustering method exist. In this paper, we propose the ANFIS4 model using a context-based fuzzy C-means (CFCM) clustering method. Context-based fuzzy C-means clustering is a clustering method that considers the characteristics of the output space as well as the input space. Here, the symmetric Gaussian membership functions are obtained by the clusters produced from each context in the design of the ANFIS4. In order to evaluate the performance of the ANFIS models according to the input space segmentation method, a prediction experiment was conducted using the combined cycle power plant (CCPP) data and the auto-MPG (miles per gallon) data. As a result of the prediction experiment, we confirmed that the ANFIS4 model using the proposed input space segmentation method shows better prediction performance than the ANFIS model (ANFIS1, ANFIS2, ANFIS3) using the existing input space segmentation method.

The Adaptive Neuro-Fuzzy Inference System (ANFIS) Application for the Ammonium Removal from Aqueous Solution Predicting by Biochar

2018 ◽  
Vol 931 ◽  
pp. 985-990
Author(s):  
Ahmed S. Khalil ◽  
Sergey V. Starovoytov ◽  
Nikolai S. Serpokrylov
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Pilot Scale ◽  
Fish Farms ◽  
Anfis Model ◽  
Data Set ◽  
Inference System ◽  
Ammonium Removal ◽  
Neuro Fuzzy

The adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the removal of ammonium () from wastewater. The ANFIS model was developed and validated with a data set from a pilot-scale of adsorption system treating aqueous solutions and wastewater from fish farms. The data sets consist of four parameters, which include pH, temperature, an initial concentration of ammonium and amount of adsorbent. The adsorbent was biochar obtained from rice straw. The ANFIS models performance was assessed through the root mean absolute error (RMSE) and was validated by testing data. The results of the study show that the adaptive neuro-fuzzy inference system (ANFIS) is able to predict the percentage of ammonium removal from adsorption column according to the input variables with acceptable accuracy, suggesting that the adaptive neuro-fuzzy inference system model is a valuable tool for estimating the quality of fish farms water. This model of ANFIS leads to cost reduction because prediction can be done without resorting to efforts that require cost and time.

scholarly journals Spike discharge prediction based on Neuro-fuzzy system

2017 ◽  
Author(s):  
Mahdi Zarei
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Subtractive Clustering ◽  
Inference System ◽  
Spike Discharge ◽  
Neuro Fuzzy ◽  
Spike Latency ◽  
Neural Fuzzy

AbstractThis paper presents the development and evaluation of different versions of Neuro-Fuzzy model for prediction of spike discharge patterns. We aim to predict the spike discharge variation using first spike latency and frequency-following interval. In order to study the spike discharge dynamics, we analyzed the Cerebral Cortex data of the cat from [29]. Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Wang and Mendel (WM), Dynamic evolving neural-fuzzy inference system (DENFIS), Hybrid neural Fuzzy Inference System (HyFIS), genetic for lateral tuning and rule selection of linguistic fuzzy system (GFS.LT.RS) and subtractive clustering and fuzzy c-means (SBC) algorithms are applied for data. Among these algorithms, ANFIS and GFS.LT.RS models have better performance. On the other hand, ANFIS and GFS.LT.RS algorithms can be used to predict the spike discharge dynamics as a function of first spike latency and frequency with a higher accuracy compared to other algorithms.

Making decision toward overseas construction projects

2019 ◽  
Vol 26 (2) ◽  
pp. 285-302 ◽  
Author(s):  
Wahyudi P. Utama ◽  
Albert P.C. Chan ◽  
Hafiz Zahoor ◽  
Ran Gao ◽  
Dwifitra Y. Jumas
Keyword(s):  
Decision Support ◽  
Construction Projects ◽  
Fuzzy Inference ◽  
Optimization Method ◽  
Preliminary Assessment ◽  
Anfis Model ◽  
Data Set ◽  
Inference System ◽  
Content Type ◽  
Neuro Fuzzy

Purpose The purpose of this paper is to introduce a decision support aid for deciding an overseas construction project (OCP) using an adaptive neuro fuzzy inference system (ANFIS). Design/methodology/approach This study presents an ANFIS approach as a decision support aid for assessment of OCPs. The processing data were derived from 110 simulation cases of OCPs. In total, 21 international factors observed from a Delphi survey were determined as assessment variables to examine the cases. The experts were involved to evaluate and judge whether the company should Go or Not Go for an OCP, based on the different parameter scenarios given. To measure the performance of the ANFIS model, root mean square error (RMSE) and coefficient of correlation (R) were employed. Findings The result shows that optimum ANFIS model indicating RMSE and R scores adequately near between 0 and 1, respectively, was obtained from parameter set of network algorithm with two input membership functions, Gaussian type of membership function and hybrid optimization method. When the model tested to nine real OCPs data, the result indicates 88.89 percent accurate. Research limitations/implications The use of simulation cases as data set in development the model has several advantages. This technique can be replicated to generate other case scenarios which are not available publicly or limited in terms of quantity. Originality/value This study evidences that the developed ANFIS model can predict the decision satisfactorily. Therefore, it can help companies’ management to make preliminary assessment of an OCP.

scholarly journals AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR FORECASTING AUSTRALIA’S DOMESTIC LOW COST CARRIER PASSENGER DEMAND

Aviation ◽  
2015 ◽  
Vol 19 (3) ◽  
pp. 150-163 ◽  
Author(s):  
Panarat Srisaeng ◽  
Glenn S. Baxter ◽  
Graham Wild
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Low Cost ◽  
Percentage Error ◽  
Subtractive Clustering ◽  
Data Set ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Partition Method

This study has proposed and empirically tested two Adaptive Neuro-Fuzzy Inference System (ANFIS) models for the first time for predicting Australia‘s domestic low cost carriers‘ demand, as measured by enplaned passengers (PAX Model) and revenue passenger kilometres performed (RPKs Model). In the ANFIS, both the learning capabilities of an artificial neural network (ANN) and the reasoning capabilities of fuzzy logic are combined to provide enhanced prediction capabilities, as compared to using a single methodology. Sugeno fuzzy rules were used in the ANFIS structure and the Gaussian membership function and linear membership functions were also developed. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. Data was normalized in order to increase the model‘s training performance. The results found that the mean absolute percentage error (MAPE) for the overall data set of the PAX and RPKs models was 1.52% and 1.17%, respectively. The highest R2-value for the PAX model was 0.9949 and 0.9953 for the RPKs model, demonstrating that the models have high predictive capabilities.

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