Interval type-2 fuzzy aggregation operator in decision making and its application

Lathamaheswari, M.; Nagarajan, D.; Kavikumar, J.

doi:10.1007/s40747-021-00287-z

Cited by 5 publications

(2 citation statements)

References 37 publications

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“…ese vulnerabilities can be presented by utilizing of T2FSs. e likelihood of a T2-fuzzy system can be calculated as a stipend of the likelihood of a Type 1 system [26,27]. A T1FS's enrollment grade is a new number in the range [0, 1].…”

Section: Type-2 Fuzzymentioning

confidence: 99%

A Type-2 Fuzzy Logic Approach for Forecasting of Effluent Quality Parameters of Wastewater Treatment

Bhattacharjee

Alattas

El-Sousy

et al. 2022

Mathematical Problems in Engineering

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In this investigation, we have studied and designed a type-2 fuzzy logic controller (IT2FLC) for the wastewater treatment plant at Haldia, India. To avoid modeling complex physical, chemical, and biological treatment processes of wastewater, this present work represents an ensemble of fuzzy models as surrogates for the wastewater treatment plant (WWTP). Using measured influent water quality data, each fuzzy model predicts water quality after the process of water treatment parameters. The pH, biological oxygen demand (BOD), total suspended particles (TSS), chemical oxygen demand (COD), and temperature are taken into account as input variables. Finally, the sensitivity of the IT2FLC model is evaluated by several statistical parameters like RMSE, MAE, MAPE, and most importantly R 2 value. For the current model, the values of the three parameters are almost 0, whereas the value of the R 2 is almost close to 1, which signifies that the IT2FLC model is accurate and more efficient in predicting response compared to other conventional methods reported in various literatures.

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Section: Type-2 Fuzzymentioning

confidence: 99%

A Type-2 Fuzzy Logic Approach for Forecasting of Effluent Quality Parameters of Wastewater Treatment

Bhattacharjee

Alattas

El-Sousy

et al. 2022

Mathematical Problems in Engineering

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show abstract

“…Furthermore, design and application of IT2 FLSs and GT2 FLSs on the basis of swarm intelligence algorithms [8,9,11,35,37,38,[47][48][49][50][51][52]] will be investigated. Future studies will be focused on the theory [39][40][41][42][53][54][55][56] of type-2 FLSs and their related algorithms.…”

Section: Simulationsmentioning

confidence: 99%

Design of sampling-based noniterative algorithms for centroid type-reduction of general type-2 fuzzy logic systems

Chen

2022

Complex Intell. Syst.

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General type-2 (GT2) fuzzy logic systems (FLSs) become a popular research topic for the past few years. Usually the Karnik–Mendel algorithms are the most prevalent approach to complete the type-reduction. Nonetheless, the iterative quality of these types of computational intensive algorithms might impede applying them. For the improved types of algorithms, some noniterative algorithms can enhance the calculation efficiencies greatly, while it is still an open problem for comparing the relation between the discrete TR algorithms and corresponding continuous TR algorithms. First, the sum and integral operations in discrete and continuous noniterative algorithms are compared. Then, three kinds of noniterative algorithms originate from the type-reduction of interval type-2 FLSs are extended to complete the centroid type-reduction of general T2 FLSs. Four computer simulations prove that while changing the number of samples suitably, the calculational results of discrete types of algorithms may accurately gain on the related continuous types of algorithms, and the calculational times of discrete types of algorithms are obviously less than the continuous types of algorithms, this may offer the possible meaning for designing and applying T2 FLSs.

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T2RFIS: type-2 regression-based fuzzy inference system

Wiktorowicz

2023

Neural Comput & Applic

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This article discusses a novel type-2 fuzzy inference system with multiple variables in which no fuzzy rules are explicitly defined. By using a rule-free system, we avoid the serious disadvantage of rule-based systems, which are burdened with the curse of dimensionality. In the proposed system, Gaussian membership functions are used for its inputs, and linearly parameterized system functions are used to obtain its output. To obtain the system parameters, a genetic algorithm with multi-objective function is applied. In the presented method, the genetic algorithm is combined with a feature selection method and a regularized ridge regression. The objective functions consist of a pair in which one function is defined as the number of active features and the other as the validation error for regression models or the accuracy for classification models. In this way, the models are selected from the Pareto front considering some compromise between their quality and simplification. Compared to the author’s previous work on the regression-based fuzzy inference system, a new inference scheme with type-2 fuzzy sets has been proposed, and the quality has been improved compared to the system based on type-1 fuzzy sets. Four experiments involving the approximation of a function, the prediction of fuel consumption, the classification of breast tissue, and the prediction of concrete compressive strength confirmed the efficacy of the presented method.

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Interval type-2 fuzzy aggregation operator in decision making and its application

Cited by 5 publications

References 37 publications

A Type-2 Fuzzy Logic Approach for Forecasting of Effluent Quality Parameters of Wastewater Treatment

A Type-2 Fuzzy Logic Approach for Forecasting of Effluent Quality Parameters of Wastewater Treatment

Design of sampling-based noniterative algorithms for centroid type-reduction of general type-2 fuzzy logic systems

T2RFIS: type-2 regression-based fuzzy inference system

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