High order <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="mml48" display="inline" overflow="scroll" altimg="si12.gif"><mml:mi mathvariant="bold-italic">α</mml:mi></mml:math>-planes integration: A new approach to computational cost reduction of General Type-2 Fuzzy Systems

Ontiveros, Emanuel; Melín, Patricia; Castillo, Oscar

doi:10.1016/j.engappai.2018.06.013

Cited by 138 publications

(24 citation statements)

References 30 publications

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“…In addition, we can also consider other time-series prediction problems, like in finance or economics. Also, regarding the model, we can optimize the structure of the neural networks using meta-heuristics, and we can use type-2 fuzzy logic in the response integration, expecting that results should improve, like in related works [ 33 , 34 ]. Finally, we envision improving the work in this paper by using adaptive fuzzy and neural network techniques, like in [ 35 , 36 ], or applying the proposed models in other kinds of applications [ 37 , 38 ].…”

Section: Discussionmentioning

confidence: 99%

Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico

et al. 2020

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In this paper, a multiple ensemble neural network model with fuzzy response aggregation for the COVID-19 time series is presented. Ensemble neural networks are composed of a set of modules, which are used to produce several predictions under different conditions. The modules are simple neural networks. Fuzzy logic is then used to aggregate the responses of several predictor modules, in this way, improving the final prediction by combining the outputs of the modules in an intelligent way. Fuzzy logic handles the uncertainty in the process of making a final decision about the prediction. The complete model was tested for the case of predicting the COVID-19 time series in Mexico, at the level of the states and the whole country. The simulation results of the multiple ensemble neural network models with fuzzy response integration show very good predicted values in the validation data set. In fact, the prediction errors of the multiple ensemble neural networks are significantly lower than using traditional monolithic neural networks, in this way showing the advantages of the proposed approach.

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Section: Discussionmentioning

confidence: 99%

Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico

et al. 2020

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“…In addition, the approach was also tested with forecasting in a window of 30 days with good results. We envision as future work applying the proposed approach on other similar problems [33] , [34] , [35] , as well as extending the use of fuzzy logic to type-2 and consider granular computing [36] , [37] , [38] , [39] , [40] , which we expect will achieve a better representation of the uncertainty in the forecasting process.…”

Section: Discussionmentioning

confidence: 99%

Forecasting of COVID-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic

Castillo

Melín

2020

Chaos, Solitons & Fractals

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117

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Highlight A hybrid intelligent approach for forecasting COVID-19 time series combining fractal theory and fuzzy logic is presented. The fractal dimension is used to measure the complexity of the dynamics in the time series of the countries in the world. Fuzzy Logic is used to represent the uncertainty in the process of making a forecast. The hybrid approach consists on a fuzzy model formed by a set of fuzzy rules that uses as input values the linear and nonlinear fractal dimensions of the time series. The outputs are the forecast for the countries based on the COVID-19 time series of confirmed cases and deaths.

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“…With the presented approach, we propose construction of a set of intuitionistic fuzzy rules using the fractal dimension of the objects to solve the problem of forecasting or recognition. As this work is in initial stages, we envision as future work testing the proposed new intuitionistic fuzzy fractal dimension in real world problems, like in plant monitoring [7], image processing [8,9], in quality control [14], intelligent control [12], parameter adaptation in meta-heuristics [15] and others [5,13,16,17].…”

Section: Discussionmentioning

confidence: 99%

A new fractal dimension definition based on intuitionistic fuzzy logic

Castillo¹,

Melín²

2019

NIFS

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In this paper we describe a method for the estimation of the fractal dimension of a geometrical object using Intuitionistic fuzzy logic techniques. The mathematical concept of fractal dimension serves to measure the geometrical complexity of an object. The algorithms for estimating the fractal dimension compute a numerical value using as input the time series data for a particular problem. The result is a crisp value (number) that defines the complexity of the geometrical object or the time series. The estimation of the fractal dimension exhibits some inherent uncertainty due to the fact that we only use a sample of points of the object, as well as due to the incomplete accuracy of the numerical algorithms for fractal dimension. For this reason, a new definition of the fractal dimension is being proposed, incorporating the concept of intuitionistic fuzzy sets, which we consider to better capture the uncertainty of the concept.

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High order α-planes integration: A new approach to computational cost reduction of General Type-2 Fuzzy Systems

Cited by 138 publications

References 30 publications

Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico

Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico

Forecasting of COVID-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic

A new fractal dimension definition based on intuitionistic fuzzy logic

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