A fuzzy inference system applied to estimate the error in the recovery of the Green’s function by means of seismic noise correlations

Rodríguez-Sánchez, José Efraín; Orozco-del-Castillo, Mauricio Gabriel; Rodríguez-Castellanos, Alejandro; Ávila-Carrera, R.; Valle-Molina, Celestino

doi:10.1088/1742-2140/aac4bf

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…Rodríguez-Sánchez et al, 2018 [8] Equations for seismic noise correlation were applied and fuzzy logic was used to estimate the error in the recovery of the Green's function.…”

Section: Gaussianmentioning

confidence: 99%

Generating Clustering-Based Interval Fuzzy Type-2 Triangular and Trapezoidal Membership Functions: A Structured Literature Review

et al. 2021

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Clustering is more popular than the expert knowledge approach in Interval Fuzzy Type-2 membership function construction because it can construct membership function automatically with less time consumption. Most research proposed a two-fuzzifier fuzzy C-Means clustering method to construct Interval Fuzzy Type-2 membership function which mainly focused on producing Gaussian membership function. The other two important membership functions, triangular and trapezoidal, are constructed using the grid partitioning method. However, the method suffers a drawback of not being able to represent actual data composition in the underlying dataset. Some research proposed triangular and trapezoidal membership functions construction using readily formed Fuzzy Type-1 membership functions, which means it remains unclear how the membership functions are heuristically constructed using fuzzy C-Means outputs. The triangular and trapezoidal membership functions are important because previous works have shown that they may produce superior performance than Gaussian membership function in some applications. Therefore, this paper presents a structured literature review on generating triangular and trapezoidal Interval Fuzzy Type-2 membership functions using fuzzy C-Means. Initially, 110 related manuscripts were collected from Web of Science, Scopus, and Google Scholar. These manuscripts went through the identification, screening, eligibility, and inclusion processes, and as a result, 21 manuscripts were reviewed and discussed in this paper. To ensure that the review also covers the important components of fuzzy logic, this paper also reviews and discusses another 49 manuscripts on fuzzy calculation and operation. Furthermore, this paper also discusses the contributions of the conducted review to the body of knowledge, future research directions and challenges, with the aim to motivate the future works of constructing the methods to generate Interval Fuzzy Type-2 triangular and trapezoidal membership functions using fuzzy C-Means. The methods imply flexibility in choosing membership function type, hence increasing the effectiveness of fuzzy applications through leveraging the advantages that each of the three membership function types could provide.

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“…Rodríguez-Sánchez et al, 2018 [8] Equations for seismic noise correlation were applied and fuzzy logic was used to estimate the error in the recovery of the Green's function.…”

Section: Gaussianmentioning

confidence: 99%

Generating Clustering-Based Interval Fuzzy Type-2 Triangular and Trapezoidal Membership Functions: A Structured Literature Review

et al. 2021

View full text Add to dashboard Cite

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“…The three main types of fuzzy membership function (MF) are Gaussian [1,2], triangular [3,4] and trapezoidal [5,6]. Each of the MFs has its own advantages and disadvantages, hence providing better performance than the other MFs in certain scenarios or applications.…”

Section: Introductionmentioning

confidence: 99%

Generating Type 2 Trapezoidal Fuzzy Membership Function Using Genetic Tuning

Fouad¹,

Hassan²,

Salim³

2022

Computers, Materials &Amp; Continua

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Fuzzy inference system (FIS) is a process of fuzzy logic reasoning to produce the output based on fuzzified inputs. The system starts with identifying input from data, applying the fuzziness to input using membership functions (MF), generating fuzzy rules for the fuzzy sets and obtaining the output. There are several types of input MFs which can be introduced in FIS, commonly chosen based on the type of real data, sensitivity of certain rule implied and computational limits. This paper focuses on the construction of interval type 2 (IT2) trapezoidal shape MF from fuzzy C Means (FCM) that is used for fuzzification process of mamdani FIS. In the process, upper MF (UMF) and lower MF (LMF) of the MF need to be identified to get the range of the footprint of uncertainty (FOU). This paper proposes Genetic tuning process, which is a part of genetic algorithm (GA), to adjust parameters in order to improve the behavior of existing system, especially to enhance the accuracy of the system model. This novel process is a hybrid approach which produces Genetic Fuzzy System (GFS) that helps to enhance fuzzy classification problems and performance. The approach provides a new method for the construction and tuning process of the IT2 MF, based on the FCM outcomes. The result is compared to Gaussian shape IT2 MF and trapezoid IT2 MF generated by the classic GA method. It is shown that the proposed approach is able to outperform the mentioned benchmarked approaches. The work implies a wider range of IT2 MF types, constructed based on FCM outcomes, and an optimum generation of the FOU so that it can be implemented in practical applications such as prediction, analytics and rule-based solutions.

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Fuzzy Logic-Based COVID-19 and Other Respiratory Conditions Pre-clinical Diagnosis System

Orozco-del-Castillo

Novelo-Cruz

Hernández-Gómez

et al. 2020

Communications in Computer and Information Science

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A fuzzy inference system applied to estimate the error in the recovery of the Green’s function by means of seismic noise correlations

Cited by 4 publications

References 22 publications

Generating Clustering-Based Interval Fuzzy Type-2 Triangular and Trapezoidal Membership Functions: A Structured Literature Review

Generating Clustering-Based Interval Fuzzy Type-2 Triangular and Trapezoidal Membership Functions: A Structured Literature Review

Generating Type 2 Trapezoidal Fuzzy Membership Function Using Genetic Tuning

Fuzzy Logic-Based COVID-19 and Other Respiratory Conditions Pre-clinical Diagnosis System

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