Designing Fuzzy Sets With the Use of the Parametric Principle of Justifiable Granularity

Pedrycz, Witold; Wang, Xianmin

doi:10.1109/tfuzz.2015.2453393

Cited by 84 publications

(36 citation statements)

References 24 publications

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“…Pedrycz [27] introduced the concept of hierarchical refined FCM clustering, proposed an algorithm, and confirmed the performance of the model in terms of the coverage and specificity. Pedrycz [28] designed a fuzzy set using the principle of granular parameters and confirmed the model's performance by justification. Zhu [29] considered the reconstruction ability of the designed information granulation system, designed a set of meaningful elliptical information granulations using the principle of granularity, and confirmed the performance of the model in terms of the coverage and specificity.…”

Section: Of 17mentioning

confidence: 86%

“…Specificity is related to the length of the triangular fuzzy number and indicates how specific and detailed the fuzzy number is. Using the coverage and specificity measures, we obtained the PI [26][27][28][29][30][31][32] as the final performance quantifier. In this paper, the predicted performances of different particle models, taken from several studies [26][27][28][29][30][31][32], were compared and analyzed using the performance evaluation method proposed by Hu [30].…”

Section: Performance Evaluation Methods Suitable For the Gmmentioning

confidence: 99%

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Performance Evaluation of Automobile Fuel Consumption Using a Fuzzy-Based Granular Model with Coverage and Specificity

Yeom

Kwak

2019

Symmetry

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The predictive performance of different granular models (GMs) was compared and analyzed for methods that evenly divide linguistic context in information granulation-based GMs and perform flexible partitioning. GMs are defined by input and output space information transformations using context-based fuzzy C-means clustering. The input space information transformation is directly induced by the output space context. Usually, the output space context is evenly divided. In this paper, the linguistic context was flexibly divided by stochastically distributing data in the output space. Unlike most fuzzy models, this GM yielded information segmentation. Their performance is usually evaluated using the root mean square error, which utilizes the difference between the model’s output and ground truth. However, this is inadequate for the performance evaluation of information innovation-based GMs. Thus, the GM performance was compared and analyzed using the linguistic context partitioning by selecting the appropriate performance evaluation method for the GM. The method was augmented by the coverage and specificity of the GMs output as the performance index. For the GM validation, its performance was compared and analyzed using the auto MPG dataset. The GM with flexible partitioning of linguistic context performed better. Performance evaluation using the coverage and specificity of the membership function was validated.

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Section: Of 17mentioning

confidence: 86%

Section: Performance Evaluation Methods Suitable For the Gmmentioning

confidence: 99%

Performance Evaluation of Automobile Fuel Consumption Using a Fuzzy-Based Granular Model with Coverage and Specificity

Yeom

Kwak

2019

Symmetry

View full text Add to dashboard Cite

show abstract

“…Several different ways to specify membership functions have been proposed. Some of the approaches have expert-based specifications (Pedrycz & Gomide, 1998;Pedrycz & Gomide, 2007), while others are data-driven (Hasuike, Katagiri, & Tsubaki, 2015a, 2015bJalota, Thakur, & Mittal, 2017;Kaufmann, Meier, & Stoffel, 2015;Pazhoumand-Dar, Lam, & Masek, 2017;Pedrycz & Wang, 2016;Pota, Esposito, & Pietro, 2013;Runkler, 2016). Although the membership function plays an important role in capturing uncertainty in the construction industry, little research has been done on the specification of membership functions in such contexts.…”

Section: Membership Function Specification Methodsmentioning

confidence: 99%

Fuzzy Hybrid Computing in Construction Engineering and Management

2018

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“…The entropy-based differently implicational algorithm was proposed in [26], which included its solving process and analysis of reversibility property. However, in [26], we did not show any specific computing example of the entropy-based differently implicational algorithm. As a result, in order to help reader to understand this algorithm in a deeper level, we add some examples here.…”

Section: Examplesmentioning

confidence: 99%