Fuzzy Linear Regression for the Time Series Data which is Fuzzified with SMRGT Method

Yalaz, Seçil; Atay, Arife

doi:10.19113/sdufbed.49849

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…and the defuzzification technique (centroid, maximum membership degree, etc.). The SMRGT method was first introduced by [8], the Mamdani fuzzy system was selected as an operator, and has been utilized successfully in numerous types of research, including those conducted by [34][35][36][37][38][39][40]. As a result, they concluded that this new method for determining membership functions (MFs) and fuzzy rules (FRs) is reliable.…”

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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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: 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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“…The experimental study was done to establish the span of breaks with fuzzy time series [50]. A non-linear optimization with polynomial time series is another work presented by authors [51]. The forecasting models based on Event discretization function were placed forward.…”

Section: Literature Reviewmentioning

confidence: 99%

Degree Approximation-Based Fuzzy Partitioning Algorithm and Applications in Wheat Production Prediction

Jain

Kapania

et al. 2018

Symmetry

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Recently, prediction modelling has become important in data analysis. In this paper, we propose a novel algorithm to analyze the past dataset of crop yields and predict future yields using regression-based approximation of time series fuzzy data. A framework-based algorithm, which we named DAbFP (data algorithm for degree approximation-based fuzzy partitioning), is proposed to forecast wheat yield production with fuzzy time series data. Specifically, time series data were fuzzified by the simple maximum-based generalized mean function. Different cases for prediction values were evaluated based on two-set interval-based partitioning to get accurate results. The novelty of the method lies in its ability to approximate a fuzzy relation for forecasting that provides lesser complexity and higher accuracy in linear, cubic, and quadratic order than the existing methods. A lesser complexity as compared to dynamic data approximation makes it easier to find the suitable de-fuzzification process and obtain accurate predicted values. The proposed algorithm is compared with the latest existing frameworks in terms of mean square error (MSE) and average forecasting error rate (AFER).

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“…Automatic fuzzy model generation using the SMRGT technique (abbreviated as SFM (SMRGT Fuzzy Modeler)) was developed by [11]. Using the fuzzy least square regression (FLSR) technique, [12] could apply it to time series data, creating an equation for making projections in the future. It was used by [13] to assess the surface profile of the water in open channels when subjected to varying hydraulic conditions.…”

Section: Introductionmentioning

confidence: 99%

Flow Coefficient Determination in Catchment Based on Analysis of Temperature and Wind Speed Data Using the Fuzzy SMRGT Method

Gunal,

Mehdi

2023

IOP Conf. Ser.: Earth Environ. Sci.

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In engineering hydrology, calculating the flow coefficient is a crucial step. The flow coefficient calculation is necessary for directing the rational profiteering of water resources, improving the overall efficiency of water resource utilization, and minimizing the effect of catastrophic events. By precisely determining the flow coefficient, which is the most influential factor in flood flow, the current issues will be mitigated substantially. Various techniques are available in the existing literature for modelling flow coefficient. Most of them, however, rely on black-box approaches that are not generalizable. Therefore, this paper applied an intelligent model based on a fuzzy logic system called the Simple Membership Function and Fuzzy Rules Generation Technique (SMRGT). The new technique considers the physical cause-effect relationship and is intended to aid individuals who struggle to choose the number, form, and logic of membership functions and fuzzy rules in any fuzzy set. The study area’s temperature and wind speed data were incorporated into the SMRGT model’s input variables. The output was the flow coefficient. The prediction made by the model was validated against observational data. The comparison relies on numerous statistics and errors. The results indicated that the SMRGT model predicts the flow coefficient extraordinarily well and is an excellent method for generating membership functions and fuzzy rules.

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Fuzzy Linear Regression for the Time Series Data which is Fuzzified with SMRGT Method

Cited by 4 publications

References 13 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

Degree Approximation-Based Fuzzy Partitioning Algorithm and Applications in Wheat Production Prediction

Flow Coefficient Determination in Catchment Based on Analysis of Temperature and Wind Speed Data Using the Fuzzy SMRGT Method

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