The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Shyiming Chen - One of the best experts on this subject based on the ideXlab platform.
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interval valued intuitionistic fuzzy multiple attribute decision making based on nonlinear Programming Methodology and topsis method
Information Sciences, 2020Co-Authors: Shyiming Chen, Shouzhen Zeng, Kangyun FanAbstract:Abstract In this paper, we propose a new multiple attribute decision making (MADM) method based on the nonlinear Programming (NLP) Methodology, the TOPSIS method and interval-valued intuitionistic fuzzy values (IVIFVs). The evaluating values of the alternatives with respect to attributes and the attributes’ weights are represented by IVIFVs. The NLP Methodology is applied to get the optimal attributes’ weights. The proposed MADM method can overcome the drawbacks of the existing MADM methods to deal with MADM problems using IVIFVs.
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multiattribute decision making based on nonlinear Programming Methodology particle swarm optimization techniques and interval valued intuitionistic fuzzy values
Information Sciences, 2019Co-Authors: Shyiming Chen, Wenhsin HanAbstract:Abstract In this paper, we propose a new multiattribute decision making (MADM) method by applying the nonlinear Programming (NLP) Methodology and particle swarm optimization (PSO) techniques using interval-valued intuitionistic fuzzy values (IVIFVs) to conquer the drawbacks of Chen and Huang's MADM method (2017), which has three drawbacks, i.e., (1) multiple different preference orders (POs) of alternatives are obtained in some situations, (2) the PO of alternatives cannot be distinguished in some circumstances, and (3) the PO of alternatives cannot be obtained in some circumstances. Moreover, the proposed MADM method also can conquer the shortcomings of Chen and Chiou's MADM method (2015), Li's MADM method (2010) and Zhitao and Yingjun's method (2011).
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multiattribute decision making based on shannon s information entropy non linear Programming Methodology and interval valued intuitionistic fuzzy values
Information Sciences, 2018Co-Authors: Shyiming Chen, Liwei Kuo, Xinyao ZouAbstract:Abstract In this paper, we propose a novel multiattribute decision making (MADM) Methodology based on Shannon's information entropy, the non-linear Programming (NLP) Methodology and interval-valued intuitionistic fuzzy values (IVIFVs), where attributes’ weights and evaluating attributes’ values with respect to alternatives are expressed by IVIFVs. Several examples are used to illustrate that the proposed MADM Methodology can conquer the drawbacks of Wang and Chen's MADM Methodology (2018) in interval-valued intuitionistic fuzzy (IVIF) environments.
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multiattribute decision making based on non linear Programming Methodology with hyperbolic function and interval valued intuitionistic fuzzy values
Information Sciences, 2018Co-Authors: Shyiming Chen, Liwei KuoAbstract:Abstract In this paper, we present a novel multiattribute decision making (MADM) method based on the non-linear Programming (NLP) Methodology with the hyperbolic tangent function and interval-valued intuitionistic fuzzy values (IVIFVs). Both the attributes’ weights and the decision matrix (DM) are expressed by IVIFVs. First, the proposed MADM method constructs the transformed decision matrix (TDM) of the DM. Then, it constructs a NLP model with the hyperbolic tangent function to get the optimal weights of the attributes. Then, it uses the interval-valued intuitionistic fuzzy weighted averaging (IVIFWA) operator to calculate the weighted evaluating IVIFVs of the alternatives. Finally, it uses Wang et al.’s method (2009) for comparing IVIFVs to obtain the preference order (PO) of the alternatives. It can conquer the shortcomings of Chen and Huang's MADM method (2017).
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a new multiple attribute decision making method based on linear Programming Methodology and novel score function and novel accuracy function of interval valued intuitionistic fuzzy values
Information Sciences, 2018Co-Authors: Chengyi Wang, Shyiming ChenAbstract:Abstract Score functions and accuracy functions of interval-valued intuitionistic fuzzy values (IVIFVs) play important roles in dealing with multiple attribute decision making (MADM) problems in interval-valued intuitionistic fuzzy (IVIF) environments. In this paper, we propose a new MADM method using the linear Programming (LP) Methodology and the proposed new score function and the proposed new accuracy function of IVIFVs for overcoming the drawbacks of Wang and Chen's MADM method (2017), which has the drawbacks that the preference order (PO) of alternatives cannot be distinguished in some cases and it gets an infinite number of solutions of the optimal weights of attributes when the summation values of some columns in the transformed decision matrix (TDM) are the same, such that it obtains different POs of alternatives.
Jiacai Liu - One of the best experts on this subject based on the ideXlab platform.
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corrections to topsis based nonlinear Programming Methodology for multi attribute decision making with interval valued intuitionistic fuzzy sets apr 10 299 311
IEEE Transactions on Fuzzy Systems, 2018Co-Authors: Jiacai LiuAbstract:There are some mistakes in the computation results of the real example in the article by Li, “TOPSIS-based nonlinear-Programming Methodology for multi-attribute decision making with interval-valued intuitionistic fuzzy sets” [ IEEE Trans. Fuzzy Syst. , vol. 18, no. 2, pp. 299–311, 2010], and this article provides corrections to that paper.
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corrections to topsis based nonlinear Programming Methodology for multi attribute decision making with interval valued intuitionistic fuzzy sets
IEEE Transactions on Fuzzy Systems, 2010Co-Authors: Jiacai LiuAbstract:Interval-valued intuitionistic fuzzy (IVIF) sets are useful to deal with fuzziness inherent in decision data and decision-making processes. The aim of this paper is to develop a nonlinear-Programming Methodology that is based on the technique for order preference by similarity to ideal solution to solve multiattribute decision-making (MADM) problems with both ratings of alternatives on attributes and weights of attributes expressed with IVIF sets. In this Methodology, nonlinear-Programming models are constructed on the basis of the concepts of the relative-closeness coefficient and the weighted-Euclidean distance. Simpler auxiliary nonlinear-Programming models are further deduced to calculate relative-closeness of IF sets of alternatives to the IVIF-positive ideal solution, which can be used to generate the ranking order of alternatives. The proposed Methodology is validated and compared with other similar methods. A real example is examined to demonstrate the applicability and validity of the Methodology proposed in this paper.
Wenhsin Han - One of the best experts on this subject based on the ideXlab platform.
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multiattribute decision making based on nonlinear Programming Methodology particle swarm optimization techniques and interval valued intuitionistic fuzzy values
Information Sciences, 2019Co-Authors: Shyiming Chen, Wenhsin HanAbstract:Abstract In this paper, we propose a new multiattribute decision making (MADM) method by applying the nonlinear Programming (NLP) Methodology and particle swarm optimization (PSO) techniques using interval-valued intuitionistic fuzzy values (IVIFVs) to conquer the drawbacks of Chen and Huang's MADM method (2017), which has three drawbacks, i.e., (1) multiple different preference orders (POs) of alternatives are obtained in some situations, (2) the PO of alternatives cannot be distinguished in some circumstances, and (3) the PO of alternatives cannot be obtained in some circumstances. Moreover, the proposed MADM method also can conquer the shortcomings of Chen and Chiou's MADM method (2015), Li's MADM method (2010) and Zhitao and Yingjun's method (2011).
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a new multiattribute decision making method based on multiplication operations of interval valued intuitionistic fuzzy values and linear Programming Methodology
Information Sciences, 2018Co-Authors: Shyiming Chen, Wenhsin HanAbstract:Abstract In this paper, we propose a new method for multiattribute decision making (MADM) using multiplication operations of interval-valued intuitionistic fuzzy values (IVIFVs) and the linear Programming (LP) Methodology. It can overcome the shortcomings of Chen and Huang's MADM method (2017), where Chen and Huang's MADM method has two shortcomings, i.e., (1) it gets an infinite number of solutions of the optimal weights of attributes when the summation values of some columns in the transformed decision matrix (TDM) are the same, resulting in the case that it obtains different preference orders (POs) of the alternatives, and (2) the PO of alternatives cannot be distinguished in some situations.
Chengyi Wang - One of the best experts on this subject based on the ideXlab platform.
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a new multiple attribute decision making method based on linear Programming Methodology and novel score function and novel accuracy function of interval valued intuitionistic fuzzy values
Information Sciences, 2018Co-Authors: Chengyi Wang, Shyiming ChenAbstract:Abstract Score functions and accuracy functions of interval-valued intuitionistic fuzzy values (IVIFVs) play important roles in dealing with multiple attribute decision making (MADM) problems in interval-valued intuitionistic fuzzy (IVIF) environments. In this paper, we propose a new MADM method using the linear Programming (LP) Methodology and the proposed new score function and the proposed new accuracy function of IVIFVs for overcoming the drawbacks of Wang and Chen's MADM method (2017), which has the drawbacks that the preference order (PO) of alternatives cannot be distinguished in some cases and it gets an infinite number of solutions of the optimal weights of attributes when the summation values of some columns in the transformed decision matrix (TDM) are the same, such that it obtains different POs of alternatives.
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an improved multiattribute decision making method based on new score function of interval valued intuitionistic fuzzy values and linear Programming Methodology
Information Sciences, 2017Co-Authors: Chengyi Wang, Shyiming ChenAbstract:Abstract In this paper, we propose an improved multiple attribute decision making (MADM) method based on the proposed new score function S WC of interval-valued intuitionistic fuzzy values (IVIFVs) and the linear Programming (LP) Methodology to overcome the drawback of Chen and Huang's method (2017) for MADM interval-valued intuitionistic fuzzy (IVIF) environments, where Chen and Huang's method has the drawback that it cannot distinguish the preference order of alternatives in some situations.
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multiple attribute decision making based on interval valued intuitionistic fuzzy sets linear Programming Methodology and the extended topsis method
Information Sciences, 2017Co-Authors: Chengyi Wang, Shyiming ChenAbstract:In recent years, some multiple attribute decision making (MADM) methods have been presented based on interval-valued intuitionistic fuzzy sets (IVIFSs). In this paper, we propose a new MADM method based on IVIFSs, the linear Programming (LP) Methodology, and the extension of the technique for order preference by similarity to ideal solution (TOPSIS) method, where the LP Methodology is used to obtain optimal weights of attributes. The proposed method can overcome the drawbacks of the existing methods for MADM in interval-valued intuitionistic fuzzy (IVIF) environments.
Themis Prodromakis - One of the best experts on this subject based on the ideXlab platform.
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multibit memory operation of metal oxide bi layer memristors
Scientific Reports, 2017Co-Authors: Spyros Stathopoulos, Ali Khiat, Maria Trapatseli, Simone Cortese, Alexantrou Serb, Ilia Valov, Themis ProdromakisAbstract:Emerging nanoionic memristive devices are considered as the memory technology of the future and have been winning a great deal of attention due to their ability to perform fast and at the expense of low-power and -space requirements. Their full potential is envisioned that can be fulfilled through their capacity to store multiple memory states per cell, which however has been constrained so far by issues affecting the long-term stability of independent states. Here, we introduce and evaluate a multitude of metal-oxide bi-layers and demonstrate the benefits from increased memory stability via multibit memory operation. We propose a Programming Methodology that allows for operating metal-oxide memristive devices as multibit memory elements with highly packed yet clearly discernible memory states. These states were found to correlate with the transport properties of the introduced barrier layers. We are demonstrating memory cells with up to 6.5 bits of information storage as well as excellent retention and power consumption performance. This paves the way for neuromorphic and non-volatile memory applications.
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multibit memory operation of metal oxide bi layer memristors
arXiv: Mesoscale and Nanoscale Physics, 2017Co-Authors: Spyros Stathopoulos, Ali Khiat, Maria Trapatseli, Simone Cortese, Alexantrou Serb, Ilia Valov, Themis ProdromakisAbstract:In this work, we evaluate a multitude of metal-oxide bi-layers and demonstrate the benefits from increased memory stability via multibit memory operation. We introduce a Programming Methodology that allows for operating metal-oxide memristive devices as multibit memory elements with highly packed yet clearly discernible memory states. We finally demonstrate a 5.5-bit memory cell (47 resistive states) with excellent retention and power consumption performance. This paves the way for neuromorphic and non-volatile memory applications.
