A New Approach to Fuzzy Soft Set Theory and Its Application in Decision Making

Tripathy, B. K.; Sooraj, T. R.; Mohanty, Ramakanta

doi:10.1007/978-81-322-2731-1_28

Cited by 23 publications

(18 citation statements)

References 12 publications

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“…Earlier FSSs were used for decision making in [5]. Some flaws in the approach were pointed out in [6] and rectifications were made. Due to the lack of information and uncertainty in real life scenarios, a single decision maker cannot able to take proper decision.…”

Section: Discussionmentioning

confidence: 99%

“…In [5] Maji et al provided an application of FSSs in a decision making system. But the algorithm given in that paper has some issues and those issues are discussed in [6]. Tripathy et al rectified the issues and provided suitable solution for the problems addressed in [3] and also introduced the concept of negative and positive parameters in [6].…”

Section: Application Of Fss In Group Decision Makingmentioning

confidence: 93%

“…Some applications of various hybrid soft set models are discussed in [1,[7][8][9][10][11][12]. This study was further extended to the context of FSSs [6] where they identified some drawbacks in [3] and took care of these drawbacks while introducing an algorithm for decision making [6]. In this paper, we have carried this study further by using FSS in handling the problem of multi-criteria group decision making.…”

Section: Introductionmentioning

confidence: 94%

“…Parameters are classified into positive parameter and negative parameter. We use the notion of negative parameter as in [6]. The priority table is as follows ( Table 3).…”

Section: Application Of Fss In Group Decision Makingmentioning

confidence: 99%

“…Following this trend Maji et al [5] put forward the concept of FSSs will systematize many operations defined upon them as done in [5]. Extending this approach further, we introduced the membership functions for FSS in [6]. Maji et al discussed an application of soft sets in decision making problems [3].…”

Section: Introductionmentioning

confidence: 97%

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Fuzzy Soft Set Theory and Its Application in Group Decision Making

Sooraj

Mohanty

Tripathy

2016

Advanced Computing and Communication Technologies

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Soft set theory was introduced by Molodtsov to handle uncertainty. It uses a family of subsets associated with each parameter. Hybrid models have been found to be more useful than the individual components. Earlier fuzzy set and soft set were combined to form fuzzy soft sets (FSS). Soft sets were defined from a different point of view in Tripathy et al. (Int J Reasoning-Based Intell Syst 7(3/4), 2015) where they used the notion of characteristic functions. Hence, many related concepts were also redefined. In Tripathy et al. (Proceedings of ICCIDM-2015 membership function for FSSs was defined. We propose a new algorithm by following this approach which provides an application of FSSs in group decision making. The performance of this algorithm is substantially improved than that of the earlier algorithm.

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Section: Discussionmentioning

confidence: 99%

Section: Application Of Fss In Group Decision Makingmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 94%

“…Parameters are classified into positive parameter and negative parameter. We use the notion of negative parameter as in [6]. The priority table is as follows ( Table 3).…”

Section: Application Of Fss In Group Decision Makingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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Fuzzy Soft Set Theory and Its Application in Group Decision Making

Sooraj

Mohanty

Tripathy

2016

Advanced Computing and Communication Technologies

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Trajectory tracking control of underactuated tendon‐driven truss‐like manipulator based on type‐1 and interval type‐2 fuzzy logic approach

Ding

Wen

et al. 2021

Int J of Intelligent Sys

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This paper deals with the trajectory tracking control problem of a planar underactuated tendon‐driven truss‐like manipulator (UTTM) by using fuzzy logic control methods, including type‐1 fuzzy and interval type‐2 fuzzy approaches. The UTTM is a novel designed underactuated robot that excels in manipulating in harsh environments due to its tendon‐driven parallelogram structure. This unique structure, however, poses challenges for UTTM's trajectory tracking controller design, including underactuation, excessive nonlinearity, parameter uncertainty, and so on. To solve these problems, a fuzzy logic‐based approach is proposed. First, the dynamic model of UTTM is transformed into a partially linearized form. Then a type‐1 fuzzy controller is designed according to a linear quadratic regulator controller for the linearized system. Then by blurring the membership functions of type‐1 fuzzy controllers, an interval type‐2 fuzzy logic controller is designed, aiming at dealing with uncertainties. The proposed type‐1 and interval type‐2 fuzzy logic controllers are validated through simulations, and made comparisons with each other, especially when the system is involved with uncertainties. Simulation results show that the interval type‐2 fuzzy‐based trajectory tracking controller provides better performance than the type‐1 counterpart in terms of tracking accuracy and capacity against uncertainties.

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