The Experts below are selected from a list of 546849 Experts worldwide ranked by ideXlab platform
Luis Martinez - One of the best experts on this subject based on the ideXlab platform.
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linguistic approaches based on the 2 tuple fuzzy linguistic Representation Model
2015Co-Authors: Luis Martinez, Rosa M RodriguezAbstract:Linguistic Modelling has been applied to decision making, among other research fields, since the beginning of the 1980s with successful and interesting results. The introduction of the 2-tuple linguistic Model opened the door to a further intensive, extensive, and deeper study of the use of linguistic information and Computing with Words by using symbolic approaches in different applications, mainly in the decision-making field and related topics. Such a study has attracted the attention of many scientists whose research concerns how to improve the use of symbolic Models for Computing with Words in linguistic decision making. As a result of such research some new symbolic approaches have been developed that try to improve different aspects of the 2-tuple linguistic Model; several of these approaches are directly based on it and aim at overcoming some specific limitations of the 2-tuple linguistic Model. This chapter presents a review of several of those symbolic approaches that are based on it and its concepts.
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fusion of multigranular linguistic information based on the 2 tuple fuzzy linguistic Representation Model
2002Co-Authors: Luis Martinez, Enrique Herreraviedma, Francisco ChiclanaAbstract:The Fuzzy Linguistic Approach has been applied successfully to many problems, its use implies processes of Computing with Words (CW). One important limitation of the fuzzy linguistic approach appears when these processes are applied to problems defined in multigranular linguistic contexts. This limitation consists of the difficulty in dealing with this type of information in processes of CW, due to the fact, that there is no standard normalization process for this type of information as in the numerical domain. In this contribution, taking as base the 2-tuple fuzzy linguistic Representation Model and its computational technique, we shall present a method for easily dealing with multigranular linguistic information in fusion processes.
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an approach for combining linguistic and numerical information based on the 2 tuple fuzzy linguistic Representation Model in decision making
International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 2000Co-Authors: Luis MartinezAbstract:In this paper we shall develop a procedure for combining numerical and linguistic information without loss of information in the transformation processes between numerical and linguistic information, taking as base for representing the information the 2-tuple fuzzy linguistic Representation Model. We shall analyze the conditions to impose the linguistic term set in order to ensure that the combination procedure does not produce any loss of information. Afterwards the aggregation process will be applied to a decision procedure over a multi-attribute decision-making problem dealing with numerical and linguistic information, that is, with qualitative and quantitative attributes.
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A 2-tuple fuzzy linguistic Representation Model for computing with words
IEEE Transactions on Fuzzy Systems, 2000Co-Authors: F. Herrera, Luis MartinezAbstract:The fuzzy linguistic approach has been applied successfully to many problems. However, there is a limitation of this approach imposed by its information Representation Model and the computation methods used when fusion processes are performed on linguistic values. This limitation is the loss of information; this loss of information implies a lack of precision in the final results from the fusion of linguistic information. In this paper, we present tools for overcoming this limitation. The linguistic information is expressed by means of 2-tuples, which are composed of a linguistic term and a numeric value assessed in (-0.5, 0.5). This Model allows a continuous Representation of the linguistic information on its domain, therefore, it can represent any counting of information obtained in a aggregation process. We then develop a computational technique for computing with words without any loss of information. Finally, different classical aggregation operators are extended to deal with the 2-tuple linguistic Model.
Jongyun Hao - One of the best experts on this subject based on the ideXlab platform.
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A new version of 2-tuple fuzzy linguistic Representation Model for computing with words
IEEE Transactions on Fuzzy Systems, 2006Co-Authors: Jin-Hsien Wang, Jongyun HaoAbstract:In this paper, we provide a new (proportional) 2-tuple fuzzy linguistic Representation Model for computing with words (CW), which is based on the concept of "symbolic proportion." This concept motivates us to represent the linguistic information by means of 2-tuples, which are composed by two proportional linguistic terms. For clarity and generality, we first study proportional 2-tuples under ordinal contexts. Then, under linguistic contexts and based on canonical characteristic values (CCVs) of linguistic labels, we define many aggregation operators to handle proportional 2-tuple linguistic information in a computational stage for CW without any loss of information. Our approach for this proportional 2-tuple fuzzy linguistic Representation Model deals with linguistic labels, which do not have to be symmetrically distributed around a medium label and without the traditional requirement of having "equal distance" between them. Moreover, this new Model not only provides a space to allow a "continuous" interpolation of a sequence of ordered linguistic labels, but also provides an opportunity to describe the initial linguistic information by members of a "continuous" linguistic scale domain which does not necessarily require the ordered linguistic terms of a linguistic variable being equidistant. Meanwhile, under the assumption of equally informative (which is defined by a condition based on the concept of CCV), we show that our Model reduces to Herrera and Martı´nez's (translational) 2-tuple fuzzy linguistic Representation Model.
Miguel Angel Sotelo - One of the best experts on this subject based on the ideXlab platform.
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a novel sparse Representation Model for pedestrian abnormal trajectory understanding
Expert Systems With Applications, 2019Co-Authors: Zhijun Chen, Hao Cai, Yishi Zhang, Miguel Angel SoteloAbstract:Abstract Pedestrian abnormal trajectory understanding based on video surveillance systems can improve public safety. However, manually identifying pedestrian abnormal trajectories is usually a prohibitive workload. The objective of this study is to propose an automatic method for understanding pedestrian abnormal trajectories. An improved sparse Representation Model, namely information entropy constrained trajectory Representation method (IECTR), is developed for pedestrian trajectory classification. It aims to reduce the entropy for trajectory Representation and to obtain superior analyzing results. In the proposed method, the orthogonal matching pursuit (OMP) is embedded in the expectation maximization (EM) method to iteratively obtain the selection probabilities and the sparse coefficients. In addition, the lower-bound sparser condition of Lp-minimization (0
Jin-Hsien Wang - One of the best experts on this subject based on the ideXlab platform.
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A new version of 2-tuple fuzzy linguistic Representation Model for computing with words
IEEE Transactions on Fuzzy Systems, 2006Co-Authors: Jin-Hsien Wang, Jongyun HaoAbstract:In this paper, we provide a new (proportional) 2-tuple fuzzy linguistic Representation Model for computing with words (CW), which is based on the concept of "symbolic proportion." This concept motivates us to represent the linguistic information by means of 2-tuples, which are composed by two proportional linguistic terms. For clarity and generality, we first study proportional 2-tuples under ordinal contexts. Then, under linguistic contexts and based on canonical characteristic values (CCVs) of linguistic labels, we define many aggregation operators to handle proportional 2-tuple linguistic information in a computational stage for CW without any loss of information. Our approach for this proportional 2-tuple fuzzy linguistic Representation Model deals with linguistic labels, which do not have to be symmetrically distributed around a medium label and without the traditional requirement of having "equal distance" between them. Moreover, this new Model not only provides a space to allow a "continuous" interpolation of a sequence of ordered linguistic labels, but also provides an opportunity to describe the initial linguistic information by members of a "continuous" linguistic scale domain which does not necessarily require the ordered linguistic terms of a linguistic variable being equidistant. Meanwhile, under the assumption of equally informative (which is defined by a condition based on the concept of CCV), we show that our Model reduces to Herrera and Martı´nez's (translational) 2-tuple fuzzy linguistic Representation Model.
Zhijun Chen - One of the best experts on this subject based on the ideXlab platform.
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a novel sparse Representation Model for pedestrian abnormal trajectory understanding
Expert Systems With Applications, 2019Co-Authors: Zhijun Chen, Hao Cai, Yishi Zhang, Miguel Angel SoteloAbstract:Abstract Pedestrian abnormal trajectory understanding based on video surveillance systems can improve public safety. However, manually identifying pedestrian abnormal trajectories is usually a prohibitive workload. The objective of this study is to propose an automatic method for understanding pedestrian abnormal trajectories. An improved sparse Representation Model, namely information entropy constrained trajectory Representation method (IECTR), is developed for pedestrian trajectory classification. It aims to reduce the entropy for trajectory Representation and to obtain superior analyzing results. In the proposed method, the orthogonal matching pursuit (OMP) is embedded in the expectation maximization (EM) method to iteratively obtain the selection probabilities and the sparse coefficients. In addition, the lower-bound sparser condition of Lp-minimization (0
