A Fuzzy Logic Approach to Measure Underweight Among Kindergarten’s Kids

Nowadays, being underweight during childhood is equally risky as being overweight. Underweight children lead to decrement in their academic performance, a social problem as well as challenging attitudes. Underweight people are likely to be less fit and active, which would also increase their cardiovascular risk and health problem. Their mind also becomes inflexible while their concentration and ability to decide are markedly weak. The purpose of this study is to evaluate the chances of having underweight among the children by using Fuzzy Logic Approach. Besides that, the comparison of effectiveness result of underweight between Body Mass Index (BMI) method and Fuzzy Logic Approach using the Mamdani method will be made. The data on weight and height of the children were collected from 3 kindergartens in Perlis. The result shows that 60 to 70 percent of children having an underweight in the range between 79.1% to 91.2% compare to the BMI method that is only 10 percent of children have underweight. It shows that Mamdani method was very effective compared to BMI Method because of the flexibility from the output control that is a smooth control function despite a wide range of input.

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Application of Neural Networks and Fuzzy Logic to the Calibration of the Seven-Hole Probe

Journal of Fluids Engineering ◽

10.1115/1.2819670 ◽

1998 ◽

Vol 120 (1) ◽

pp. 95-101 ◽

Cited By ~ 19

Author(s):

O. K. Rediniotis ◽

G. Chrysanthakopoulos

Keyword(s):

Neural Network ◽

Neural Networks ◽

Fuzzy Logic ◽

Middle Layer ◽

Multi Layer Perceptron ◽

Fuzzy Logic Systems ◽

Calibration Methods ◽

Wide Range ◽

Fuzzy Logic Approach ◽

Logic Approach

The theory and techniques of Artificial Neural Networks (ANN) and Fuzzy Logic Systems (FLS) are applied toward the formulation of accurate and wide-range calibration methods for such flow-diagnostics instruments as multi-hole probes. Besides introducing new calibration techniques, part of the work’s objective is to: (a) apply fuzzy-logic methods to identify systems whose behavior is described in a “crisp” rather than a “linguistic” framework and (b) compare the two approaches, i.e., neural network versus fuzzy logic approach, and their potential as universal approximators. For the ANN approach, several network configurations were tried. A Multi-Layer Perceptron with a 2-node input layer, a 4-node output layer and a 7-node hidden/middle layer, performed the best. For the FLS approach, a system with center average defuzzifier, product-inference rule, singleton fuzzifier, and Gaussian membership functions was employed. The Fuzzy Logic System seemed to outperform the Neural Network/Multi-Layer Perceptron.

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Fuzzy-Logic Approach to Estimate the Passengers’ Preference when Choosing a Bus Line within the Public Transport System

Communications - Scientific letters of the University of Zilina ◽

10.26552/com.c.2021.3.a150-a157 ◽

2021 ◽

Vol 23 (3) ◽

pp. A150-A157

Author(s):

Vitalii Naumov ◽

Baurzhan Zhamanbayev ◽

Dinara Agabekova ◽

Zhumazhan Zhanbirov ◽

Igor Taran

Keyword(s):

Fuzzy Logic ◽

Transport System ◽

Public Transport ◽

Optimization Problems ◽

The Public ◽

Public Transport System ◽

Wide Range ◽

Fuzzy Logic Approach ◽

Logic Approach ◽

The Given

For the developed system of public transport, the passengers, as the customers, have a variety of alternatives when choosing the transport mode or even the route for the given mode of public transport. The estimation of the passengers’ preference is the key task for transportation planners for solving the wide range of optimization problems in the field of public transport. A methodology for estimation of the passengers’ preference when choosing the bus line within a public transport system is developed in this paper. The proposed approach is based on the fuzzy-logic mathematical apparatus and uses the surveys’ data to calculate the membership functions defining the passengers’ preference. The case study of the passengers’ survey, held in Talas (Kazakhstan), is used to illustrate the developed methodology.

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