Robustness of full implication algorithms based on interval-valued fuzzy inference

Luo, Minxia; Zhang, Kai

doi:10.1016/j.ijar.2015.05.006

Cited by 38 publications

(8 citation statements)

References 33 publications

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“…Analogously, we can get the derivative of F (α) in the subtraction direction of α, which is the same as that in the formula (5).…”

Section: Derivative and Differential Operations Of Iffsmentioning

confidence: 95%

“…Fuzzy sets (FSs) have played an important role in amounts of domains of modern society. For the purpose of expanding the capability of FSs and expressing the uncertain information more clearly, plenty of different expressions have been proposed, such as intuitionistic fuzzy set (IFS), interval‐value fuzzy set, general type‐2 fuzzy set, hesitant fuzzy set, fuzzy linguistic approach, and so on. These sets and approaches can express information more comprehensively and specifically.…”

Section: Introductionmentioning

confidence: 99%

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The Properties of Continuous Pythagorean Fuzzy Information

Gou

Zhang

Ren

2015

Int. J. Intell. Syst.

212

102

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In practical decision‐making processes, we can utilize various types of fuzzy sets to express the uncertain and ambiguous information. However, we may encounter such the situations: the sum of the support (membership) degree and the against (nonmembership) degree to which an alternative satisfies a criterion provided by the decision maker may be bigger than 1 but their square sum is equal to or less than 1. The Pythagorean fuzzy sets (PFS), as the generalization of the fuzzy sets, can be used to effectively deal with this issue. Therefore, to enrich the theory of PFS, it is very necessary to investigate the fundamental properties of Pythagorean fuzzy information. In this paper, we first describe the change values of Pythagorean fuzzy numbers (PFNs), which are the basic components of PFSs, when considering them as variables. Then we divide all the change values into the eight regions by using the basic operations of PFNs. Finally, we develop several Pythagorean fuzzy functions and study their fundamental properties such as continuity, derivability, and differentiability in detail.

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“…Analogously, we can get the derivative of F (α) in the subtraction direction of α, which is the same as that in the formula (5).…”

Section: Derivative and Differential Operations Of Iffsmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

The Properties of Continuous Pythagorean Fuzzy Information

Gou

Zhang

Ren

2015

Int. J. Intell. Syst.

212

102

View full text Add to dashboard Cite

show abstract

“…The fuzzy theory can process imprecise information by degree of membership and is a universal approximator for non-linear mapping between input vectors and scalar outputs in terms of firing fuzzy rules to some degree [36,37]. A fuzzy logic (FL) controller is the application of the fuzzy theory.…”

Section: Fuzzy Logic Controller Designmentioning

confidence: 99%

A Fast Multi-Switched Inductor Balancing System Based on a Fuzzy Logic Controller for Lithium-Ion Battery Packs in Electric Vehicles

et al. 2017

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Based on a low cost multi-switched inductor balancing circuit (MSIBC), a fuzzy logic (FL) controller is proposed to improve the balancing performances of lithium-ion battery packs instead of an existing proportional-integral (PI) controller. In the proposed FL controller, a cell's open circuit voltages (OCVs) and their differences in the pack are used as the inputs, and the output of the FL controller is the balancing current. The FL controller for the MSIBC has the advantage of maintaining high balancing currents over the existing PI controller in almost the entire balancing process for different lithium battery types. As a result, the proposed FL controller takes a much shorter time to achieve battery pack balancing, and thus more pack capacity can be recovered. This will help to improve the pack performance in electric vehicles and extend the serving time of the battery pack.

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“…Li et al [8] extended CRI method to the case of interval-valued fuzzy set and discussed the robustness of interval-valued CRI method. Luo et al [11,12] extended triple I method and reverse triple I method to the case of interval-valued fuzzy sets and studied their robustness respectively. These researches of robustness of interval-valued fuzzy reasoning methods are based on moore metric.…”

Section: Introductionmentioning

confidence: 99%