Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification

Guzman, Juan Carlos; Miramontes, Ivette; Melín, Patricia; Prado-Arechiga, German

doi:10.3390/axioms8010008

Cited by 44 publications

(12 citation statements)

References 35 publications

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“…The particular parameter values for the membership functions were defined considering the three possible fuzzy values, which are Low, Medium, and High, assigned and adjusted in a manual way [28][29][30][31][32]. Figure 7 shows the comparison of results of Confirmed Cases Prediction in Mexico using different neural network models; two of them are a monolithic model, FITNET, and NAR versus the Modular Neural Network, which uses a fuzzy logic integrator.…”

Section: Knowledge Representation Of the Fuzzy Systemmentioning

confidence: 99%

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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: Knowledge Representation Of the Fuzzy Systemmentioning

confidence: 99%

Section: Knowledge Representation Of the Fuzzy Systemmentioning

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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“…25,26 A unique mechanism for performing BP level classification using Mamdani type has been discussed. 27 Different type-1 and type-2 fuzzy systems for classification have been used, and it has also been proved that type-2 system has a better classification rate than type 1.…”

Section: Related Workmentioning

confidence: 99%

A proposed health monitoring system using fuzzy inference system

Ghosh

Roy

Merdji

2020

Proc Inst Mech Eng H

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Due to the busy schedule of every human being in today’s world, consciousness towards one’s health has become quite alarming. A person suffering from any chronic disease needs a gradual, regular and close monitoring to recover from the disease or to be under control. Because of heavy work pressure, anxiety, change of weather and location or due to some other causes, the effect of the diseases can turn up into an appalling state. Two vital aspects of human diseases are blood pressure (hypertension) and blood sugar imbalance. Hypertension is one of the complications of prolonged untreated diabetes. Other organs like kidney, eye and peripheral nerves are also involved. The various gradations of hypertension and diabetes are required to understand for the progression of the disease and to make plan for the treatment. So these two aspects are considered in this article. The idea is to develop a logic, which could be incorporated in a pocket friendly device in future that would generate an alarm whenever there is imbalance in blood sugar or blood pressure levels. The concept of the fuzzy inference rule and first-order logic is implemented to develop this study.

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“…An IT2FIS technique is suitable for dealing with these uncertainties [47,48]. This ability is supported by the fact that the third type-2 fuzzy logic [49][50][51] sets dimensions, and its footprint of uncertainty is sufficient in comparison with what type-1 fuzzy logic [52] sets in modeling on uncertainty. IT2FIS is suitable for real world applications regarding the control of mobile robots [53,54].…”

Section: Literature Reviewmentioning

confidence: 99%

Visual-Servoing Based Global Path Planning Using Interval Type-2 Fuzzy Logic Control

Dirik

Castillo

Kocamaz

2019

Axioms

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Mobile robot motion planning in an unstructured, static, and dynamic environment is faced with a large amount of uncertainties. In an uncertain working area, a method should be selected to address the existing uncertainties in order to plan a collision-free path between the desired two points. In this paper, we propose a mobile robot path planning method in the visualize plane using an overhead camera based on interval type-2 fuzzy logic (IT2FIS). We deal with a visual-servoing based technique for obstacle-free path planning. It is necessary to determine the location of a mobile robot in an environment surrounding the robot. To reach the target and for avoiding obstacles efficiently under different shapes of obstacle in an environment, an IT2FIS is designed to generate a path. A simulation of the path planning technique compared with other methods is performed. We tested the algorithm within various scenarios. Experiment results showed the efficiency of the generated path using an overhead camera for a mobile robot.

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Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification

Cited by 44 publications

References 35 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

A proposed health monitoring system using fuzzy inference system

Visual-Servoing Based Global Path Planning Using Interval Type-2 Fuzzy Logic Control

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