Fuzzy Time-Series Models Based on Intuitionistic Fuzzy, Rough Set Fuzzy, and Differential Evolution

Deb, Partha Pratim; Bhattacharya, Diptendu; Chatterjee, Indranath

doi:10.1007/978-981-16-8892-8_10

Cited by 2 publications

(1 citation statement)

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“…Using fuzzy set theory, analyzing and classifying a bio-retention system is proposed by Galleto et al [37]. Theory to Practice there is some (IFRS) is discussed by Singh et al [38]. It is explained how to use the IFS, FRS, and differential evolution-based fuzzy time-series models proposed by deb et al [39].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach Toward Q-Rung Orthopair Fuzzy Rough Dombi Aggregation Operators and Their Application to Decision-Making Problems

Khan

Khan³

et al. 2023

IEEE Access

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It is the purpose of this paper to present and explain the fuzzy set of concepts Zadeh that are discussed in membership grade. We also demonstrate that three-way analysis is crucial in decision-making systems. In order to implement a three-way decision, various elements are required, such as conditional probability, estimated loss, and decision rules. The objective is to describe first the Q-ROFRS (Q-Rung Orthopair Fuzzy Rough Set) to define some fundamental operational rules under the dombi norms. The use of these operation laws can allow us to define some aggregation operators, which include Q-ROFWA (Q-Rung Orthopair Fuzzy Rough Weighted Average), Q-ROFROWA (Q-Rung Orthopair Fuzzy Rough Order Weighted Average), and Q-ROFHWA, which are all based on Q-Rung Orthopair Fuzzy Rough Hybrid Weighted Averages. By applying these operation laws, we can use these operator laws to calculate expert information that can be utilized in the calculation process. Ultimately, we have applied all three-way process rules to rank and find the best results possible. To determine the accuracy and relevance of each piece of work, we conducted a comparative analysis with other recent works that have been published

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Section: Introductionmentioning

confidence: 99%