Optimization of the Fuzzy Integrators in Ensembles of ANFIS Model for Time Series Prediction: The case of Mackey-Glass

Soto, Jesús; Melín, Patricia

doi:10.2991/ifsa-eusflat-15.2015.140

Cited by 11 publications

(13 citation statements)

References 25 publications

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“…An ensemble neural network is composed of various monolithic artificial neural networks (also known as modules). All the artificial neural networks are trained for the same task (Hansen and Salomon 1990;Soto et al 2015), becoming each neural network an expert of the same problem, where each one provides an answer; these answers can differ, in this work; for example, each artificial neural network provides a different prediction; even each one had learned the same information. For this reason, to obtain a final answer or decision, each answer is combined with the other answers using a unit integration .…”

Section: Ensemble Neural Networkmentioning

confidence: 99%

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RETRACTED ARTICLE: Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction

et al. 2021

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In this paper, the latest global COVID-19 pandemic prediction is addressed. Each country worldwide has faced this pandemic differently, reflected in its statistical number of confirmed and death cases. Predicting the number of confirmed and death cases could allow us to know the future number of cases and provide each country with the necessary information to make decisions based on the predictions. Recent works are focused only on confirmed COVID-19 cases or a specific country. In this work, the firefly algorithm designs an ensemble neural network architecture for each one of 26 countries. In this work, we propose the firefly algorithm for ensemble neural network optimization applied to COVID-19 time series prediction with type-2 fuzzy logic in a weighted average integration method. The proposed method finds the number of artificial neural networks needed to form an ensemble neural network and their architecture using a type-2 fuzzy inference system to combine the responses of individual artificial neural networks to perform a final prediction. The advantages of the type-2 fuzzy weighted average integration (FWA) method over the conventional average method and type-1 fuzzy weighted average integration are shown.Keywords Ensemble neural networks Á COVID-19 Á Time series prediction Á Type-2 fuzzy logic Á Firefly algorithm Communicated by V. E. Balas.

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Section: Ensemble Neural Networkmentioning

confidence: 99%

“…Figure 3 shows a representation of an ensemble neural network. We used this kind of neural network because it has been an excellent tool for time series prediction Soto et al 2015), each neural network gives us a prediction, and through an integration method, a final prediction is obtained.…”

Section: Ensemble Neural Networkmentioning

confidence: 99%

RETRACTED ARTICLE: Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction

et al. 2021

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“…Equations (10) and (11) are the same as (6) and (7), respectively, but in this case the best solution is represented by the leaders of the pack and in this case alpha, beta, and delta, as we mentioned above.…”

Section: Grey Wolfmentioning

confidence: 99%

“…The main goal of the hybridization between both methods [6] is to take advantage of their main features and in this paper we present the following features of each method that we are using for achieving hybridization:…”

Section: Proposed Fwa-gwomentioning

confidence: 99%

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A New Hybridization Approach between the Fireworks Algorithm and Grey Wolf Optimizer Algorithm

Barraza

Rodríguez

Castillo

et al. 2018

Journal of Optimization

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The main aim of this paper is to present a new hybridization approach for combining two powerful metaheuristics, one inspired by physics and the other one based on bioinspired phenomena. The first metaheuristic is based on physics laws and imitates the explosion of the fireworks and is called Fireworks Algorithm; the second metaheuristic is based on the behavior of the grey wolf and belongs to swarm intelligence methods, and this method is called the Grey Wolf Optimizer algorithm. For this work we studied and analyzed the advantages of the two methods and we propose to enhance the weakness of both methods, respectively, with the goal of obtaining a new hybridization between the Fireworks Algorithm (FWA) and the Grey Wolf Optimizer (GWO), which is denoted as FWA-GWO, and that is presented in more detail in this work. In addition, we are presenting simulation results on a set of problems that were tested in this paper with three different metaheuristics (FWA, GWO, and FWA-GWO) and these problems form a set of 22 benchmark functions in total. Finally, a statistical study with the goal of comparing the three different algorithms through a hypothesis test ( -test) is presented for supporting the conclusions of this work.

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Optimal Number of Clusters Finding Using the Fireworks Algorithm

Barraza

Valdez

Melín

et al. 2019

Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine

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Optimization of the Fuzzy Integrators in Ensembles of ANFIS Model for Time Series Prediction: The case of Mackey-Glass

Cited by 11 publications

References 25 publications

RETRACTED ARTICLE: Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction

RETRACTED ARTICLE: Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction

A New Hybridization Approach between the Fireworks Algorithm and Grey Wolf Optimizer Algorithm

Optimal Number of Clusters Finding Using the Fireworks Algorithm

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