Teaching performance evaluation by means of a hierarchical multifactorial evaluation model based on type-2 fuzzy sets

Multifactorial condition assessment for power transformers

“…The factor space theory, which were initially introduced by Wang and Li [28,29], provides a mathematical framework to solve the structure and selection of a domain and variable [30]. The factor space theory has been successfully applied to decision-making, which is used to evaluate objects in a fuzzy decision-making environment with multiple factors and provide explanations for decision results [31].…”

R-Fuzzy Sets in Factor Space

“…In the early 1980s, Professor Peizhuang Wang introduced the concept of factor spaces. In 1982, he published the first article on factor spaces (Garg & Arora 2018;Van Hoof et al 2018;Zhou et al 2017), and subsequently this concept was further developed and applied. However, factor spaces were introduced based on Zadeh fuzzy sets, whereas the fuzzy decision and control of (-∞, 0) and (1, +∞) are also important.…”

A Novel Multiple Criteria Decision-Making Method Using a Likelihood-Based Compromise Approach Under Complex Uncertainty

“…IT2 FSs are a simplified version of T2 FSs, and the membership grades of IT2 FSs are crisp intervals (Siminski, 2017;. The theory of IT2 FSs has been well developed in the literature and has been applied productively in the field related to MCDA under uncertainty (Celik and Taskin Gumus, 2016;Chen, 2014a;2014b; Lai and Chen, 2015;Singh and Garg, 2017;Wang and Chen, 2014;Zhang and Zhang, 2013;Zhong and Yao, 2017;Zhou et al, 2017). In particular, interval type-2 trapezoidal fuzzy numbers (IT2 TrFNs), as a special case of IT2 FSs, can efficiently express linguistic ratings and evaluations by objectively transforming them into numerical variables (Chen, 2017;Zhang and Zhang, 2013).…”

“…In fuzzy community, Zadeh (1975) introduced fuzzy sets of Type-2, later shortened by others to type-2 fuzzy sets (T2 FSs) and fuzzy sets with interval-valued membership functions, which was later shortened by others to interval-valued fuzzy sets (Mendel, 2007;2010). The concept of T2 FSs is an extension of type-1 fuzzy sets (T1 FSs) and is characterized by a fuzzy membership function, where the degree of membership for any element in this set is a fuzzy number in the interval [0, 1] (Chen, 2013; 2017; Zhou et al, 2017). T2 FSs are better than T1 FSs for handling imprecision and uncertainties by modeling vagueness and unreliability of information (Chen, 2017;Zhou et al, 2017).…”

“…The concept of T2 FSs is an extension of type-1 fuzzy sets (T1 FSs) and is characterized by a fuzzy membership function, where the degree of membership for any element in this set is a fuzzy number in the interval [0, 1] (Chen, 2013; 2017; Zhou et al, 2017). T2 FSs are better than T1 FSs for handling imprecision and uncertainties by modeling vagueness and unreliability of information (Chen, 2017;Zhou et al, 2017). Real-world decisions always require the use of more precise and accurate data (Lai and Chen, 2015); thus, T2 FSs are appropriate for addressing real-life problems if there is insufficient knowledge or experience (Chen, 2014a;2014b;Zhang and Zhang, 2013).…”