Multivariate modeling and type-2 fuzzy sets

Aisbett, Janet; Rickard, John T.; Morgenthaler, David G.

doi:10.1016/j.fss.2010.10.001

Cited by 34 publications

(14 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The concept of constraining the embedded membership functions resulting from type-2 inference has previously been suggested by Aisbett et al [9]. In this paper, the authors propose a novel method of generating type-2 fuzzy sets based on multivariate modelling, in which "the multivariate modeling underpinning the type-2 fuzzy sets can also constrain realizable forms of membership functions".…”

Section: Discussionmentioning

confidence: 99%

Constrained type-2 fuzzy sets

Garibaldi

Guadarrama

2011

2011 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems (T2FUZZ)

View full text Add to dashboard Cite

Abstract-Type-2 fuzzy sets extend the expressive capabilities of type-1 fuzzy sets in that, in addition to represent imprecise concepts, they are able to represent the imprecision in the membership function of fuzzy sets. Their use is particularly appropriate when modelling linguistic concepts such as words that mean slightly different things to different people. However, type-2 fuzzy sets do not place any constraints upon the continuity and other properties of their embedded sets. We argue that, for some concepts, additional properties constraining both the allowable footprint of uncertainty and the embedded sets of type-2 sets are desirable. We present a novel formulation of a constrained type-2 fuzzy set that achieves this, and then detail some of the properties of this novel form of type-2 fuzzy sets.

show abstract

Section: Discussionmentioning

confidence: 99%

Constrained type-2 fuzzy sets

Garibaldi

Guadarrama

2011

2011 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems (T2FUZZ)

View full text Add to dashboard Cite

show abstract

“…A type-2 fuzzy set (T2FS) is a membership function represented by an interval fuzzy set [0,1]. T2FS contain membership values that are crisp intervals and are the most widely used of the higher order fuzzy sets because of their relative simplicity (Zadeh 1965(Zadeh , 1975Wu, Mendel 2007b;Aisbett et al 2011;Chen 2013a).…”

Section: In Many Decision Making Problems the Decision Maker's (Dm) mentioning

confidence: 99%

A Fuzzy Multi-Criteria Decision Making Methodology Based Upon the Interval Type-2 Fuzzy Sets for Evaluating Renewable Energy Alternatives in Turkey

Balın

Baraçlı

2015

Technological and Economic Development of Economy

View full text Add to dashboard Cite

Abstract.Energy is substantial for social development and has an essential role for the survival and development of human as an environmental factor. Due to the serious increase ofthe world's primary energy price and rapid development of Turkey's economy, energy consumption of Turkey has become a major problem. For that purpose, opinions of the decision makers upon the relative importance of selection criteria are determined by using a fuzzy AHP procedure based upon type-2 fuzzy sets, and fuzzy multi-criteria decision-making based upon the interval type-2 TOPSIS method is used to rank the best renewable alternative energy. In this paper, a fuzzy multi criteria decision making (MCDM) methodology was proposed to determine the best renewable energy alternatives for Turkey, because type-2 fuzzy sets provide additional degrees of independence to represent the uncertainty and the fuzziness of real world applications. The results showed that wind was the best alternative for Turkey's energy investments, as being followed subsequently by solar, biomass, geothermal, hydraulic and hydrogen. The results were based upon the opinions of three energy planning experts who ranked the energy alternatives according to the ten criteria we have selected.

show abstract

“…Relative to mathematical research and understanding the phenomenon of uncertainty, the integration of information using fuzzy inference techniques pervades many scientific disciplines, such as multivariate and type-2 fuzzy sets; bipolar models [10,11]; and probability and possibility issues [9,27]. Information fusion is the merging of information from disparate sources with differing conceptual, contextual, and typographical representations.…”

Section: Introductionmentioning

confidence: 99%

Multi-source information fusion model in rule-based Gaussian-shaped fuzzy control inference system incorporating Gaussian density function

Cao

et al. 2015

IFS

View full text Add to dashboard Cite

Abstract. An increasing number of applications require the integration of data from various disciplines, which leads to problems with the fusion of multi-source information. In this paper, a special information structure formalized in terms of three indices (the central presentation, population or scale, and density function) is proposed. Single and mixed Gaussian models are used for single source information and their fusion results, and a parameter estimation method is also introduced. Furthermore, fuzzy similarity computing is developed for solving the fuzzy implications under a Mamdani model and a Gaussian-shaped density function. Finally, an improved rule-based Gaussian-shaped fuzzy control inference system is proposed in combination with a nonlinear conjugate gradient and a Takagi-Sugeno (T-S) model, which demonstrated the effectiveness of the proposed method as compared to other fuzzy inference systems.

show abstract

Multivariate modeling and type-2 fuzzy sets

Cited by 34 publications

References 23 publications

Constrained type-2 fuzzy sets

Constrained type-2 fuzzy sets

A Fuzzy Multi-Criteria Decision Making Methodology Based Upon the Interval Type-2 Fuzzy Sets for Evaluating Renewable Energy Alternatives in Turkey

Multi-source information fusion model in rule-based Gaussian-shaped fuzzy control inference system incorporating Gaussian density function

Contact Info

Product

Resources

About