Genetic algorithm simulation approach to determine membership functions of fuzzy traffic controller

Kim, J.W.; Kim, B.M.; Kim, J.Y.

doi:10.1049/el:19981369

Cited by 10 publications

(5 citation statements)

References 2 publications

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“…The fact that the coefficient of determination is so high (R 2 = 0.966) suggests that the statistical relationship that exists between the model and the data can be expressed in a mathematical manner. Figure (9)(10)(11) illustrate how the model result-a dependent variable called the flow coefficient-varies in three dimensions as a function of the model's independent variables (Amount of rainfall, temperature, and humidity, as well as slope and land use). This variation is shown in three-dimensional space.…”

Section: Discussionmentioning

confidence: 99%

“…After the MF types have been chosen, the problem then becomes one of optimizing the number of MFs and FRs as well as their logic and the shape they take. The construction of MFs and the simple generation of FRs has recently seen the development of a large number of methods and algorithms, including genetic algorithms (GA) [9][10][11][12][13][14][15][16], the combined use of GAs and artificial neural networks (ANNs) [17,18], ANNs [19][20][21][22][23], Kalman filters [24], probability measurement [25][26][27][28][29][30][31], and a great number of others. Many academics have proposed methods for modifying or optimizing only the number of MFs [9 -10,19,20,24-33], while others present methods for identifying only the FRs [21][22][23].…”

Section: Methodsmentioning

confidence: 99%

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Application of a new fuzzy logic model known as "SMRGT" for estimating flow coefficient rate

GÜNAL,

MEHDİ

2024

Turkish Journal of Engineering

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Since we all have our own set of limitations when it comes to perceiving the world and reasoning profoundly, we are constantly met with uncertainty as a result of a lack of information (lexical impression, incompleteness), as well as specific measurement inaccuracies. It has been found that uncertainty, which shows up as ambiguity, is the root cause of complexity, which is everywhere in the real world. Most of the uncertainty in civil engineering systems comes from the fact that the constraints (parameters) are hard to understand and are described in a vague way. The ambiguity comes from a number of sources, including physical arbitrariness, statistical uncertainty due to using limited information to estimate these characteristics, and model uncertainty due to using overly simplified methods and idealized depictions of actual performances. Thus, It is better to combine fuzzy set theory and fuzzy logic. Fuzzy logic is well-suited to modeling the indeterminacy and ambiguity that result from multiple factors and a lack of data. In order to improve upon a previous predictive model, this paper makes use of a smart model built on a fuzzy logic system (FLS). Precipitation, temperature, humidity, slope, and land use data were all taken into account as input variables in the fuzzy model. Toprak's original explanation of the simple membership function and fuzzy rules generation technique (SMRGT) was based on the fuzzy-Mamdani methodology, and used the flow coefficient as its output. The model's results were compared to available data. The following factors were considered in the comparison: 1) The maximum, minimum, mean, standard deviation, skewness, variation, and correlation coefficients are the seven statistical parameters. 2) Four types of error criteria: Mean Absolute Relative Error (MARE), Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). 3) Scatter diagram.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Application of a new fuzzy logic model known as "SMRGT" for estimating flow coefficient rate

GÜNAL,

MEHDİ

2024

Turkish Journal of Engineering

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“…Kissi et al [44] and Kim et al [45] determined MFs using GA for structure-odor modeling and fuzzy traffic controllers, respectively. However, the authors did not propose a new generalized method for use in any natural event modeling.…”

Section: Existing Literature On Fuzzy Modelingmentioning

confidence: 99%

Flow Discharge Modeling in Open Canals Using a New Fuzzy Modeling Technique (SMRGT)

Toprak¹

2009

CLEAN Soil Air Water

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The main goal of this study was to research the answer to two important questions in flow modeling; i) how to optimally design the cross-section of an open channel for a given flow, and ii) in the case of selecting the fuzzy method for modeling, how to construct the membership functions (MFs) and fuzzy rules (FRs) such that the system yields the best results. The first question is answered in order to minimize difficulties in excavation and related costs by using the appropriate flow velocity. To provide the best answer researchers use several methods. The second question is answered in order to minimize model error. For this aim, there are many algorithms proposed by researchers in the literature. In this paper, the fuzzy logic method was used for open canal flow modeling. Furthermore, a simple membership function and fuzzy rule generation technique (SMRGT) is introduced, and used for fuzzy modeling. Two fuzzy models, each for different cross-sectional shape, are presented in this study as an application of SMRGT. The comparison depends on various statistics, mean absolute relative error, and contour maps showed that the fuzzy models were successful in open channel flow modeling and SMRGT is useful for MF (membership function) and FR (fuzzy rule) generation.

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“…In fuzzy modeling, the correct determination of membership functions is of primary importance for the success of the model. It is possible to encounter many approaches in the literature about the selection of membership functions [3][4][5][6][7][8][9][10][11][12][13][14][15]. The fact that the value ranges that are close to the value determined in the approaches to determine the membership function value range also affects the system and has caused the need to be evaluated in the system at their neglected values.…”

Section: Introductionmentioning

confidence: 99%

In Dynamic Systems with Fuzzy A - Cutting Determination of Membership Function Ranges

Topaloğlu¹,

Pehlivan²

2020

nat

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Uncertainties and inaccuracies in the membership function value ranges defined by the expert in dynamic systems cause serious errors in system output. In this study, the fuzzy α-cutting technique was used to determine the ranges of membership functions on the universal cluster and neighborhood values of normal values were calculated for different α cutting coefficients and then neighborhood values were adjusted according to determined step values. Thus, while determining the value range of membership function in dynamic systems, it will be possible to talk about its neighborhood in the values that serve the same purpose. Operation in the dynamic process as wind power installation for Turkey wind energy interval value set in the potential atlas used and α cutting techniques of the gap on the universal set of the determined value with re-calculation and determination are provided.

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Genetic algorithm simulation approach to determine membership functions of fuzzy traffic controller

Cited by 10 publications

References 2 publications

Application of a new fuzzy logic model known as "SMRGT" for estimating flow coefficient rate

Application of a new fuzzy logic model known as "SMRGT" for estimating flow coefficient rate

Flow Discharge Modeling in Open Canals Using a New Fuzzy Modeling Technique (SMRGT)

In Dynamic Systems with Fuzzy A - Cutting Determination of Membership Function Ranges

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