Yazar "Deng, Yong" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim Yok
    Öğe
    Conjunctive and disjunctive combination rules in random permutation set theory: A layer-2 belief structure perspective
    (Elsevier, 2024) Zhou, Qianli; Cui, Ye; Pedrycz, Witold; Deng, Yong
    In uncertainty reasoning, conjunctive and disjunctive combination rules are the core tools for information updates. As an extension of evidential reasoning, random permutation set reasoning models uncertain information based on ordered focal sets. Existing combination rules in random permutation set theory, orthogonal sums, do not satisfy the commutativity of one of the original distributions and overly obey its order information. In this paper, the random permutation set theory is interpreted as an refined extension of Dempster-Shafer theory, and a layer-2 belief structure is proposed to describe the permutation event space. Compared with the traditional belief structure, the proposed structure can model both symbolic and numerical uncertainty. Based on the above, the conjunctive and disjunctive combination rules in Dempster-Shafer theory are extended to random permutation set theory. Through properties analysis and simulation demonstration, we demonstrate that the proposed methods can not only resolve the counter-intuitive results of orthogonal sums, but make full use of order information in distributions as well. In addition, we also extend the product space operations and discounting methods based on the proposed methods, and give a general framework of multi-source information fusion under the random permutation set theory.
  • Küçük Resim Yok
    Öğe
    Information Granule Based Uncertainty Measure of Fuzzy Evidential Distribution
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Zhou, Qianli; Pedrycz, Witold; Liang, Yingying; Deng, Yong
    Quantifying the uncertainty of information distributions containing randomness, imprecision, and fuzziness is the premise of processing them. A useful information representation in the field of intelligent computing are information granules, which optimize data from the perspective of specificity and coverage. We introduce information granularity into evidential information and model the basic probability assignment (BPA) as a weighted information granules model. Based on the proposed model, a new uncertainty measure of BPA is derived from the quality evaluation of granules. In addition, the proposed measure is extended to fuzzy evidential information distributions. When the Fuzzy BPA (FBPA) degenerates into the Probability Mass Function (ProbMF) and Possibility Mass Function (PossMF), the proposed method degenerates to Gini entropy and Yager's specificity measure, respectively. We use a refined belief structure to interpret the meaning of FBPA in the transfer belief model, and verify the validity of the proposed method by analyzing its properties and presenting numerical examples. The concept of information granule is used for the first time to model focal set and beliefs. Compared with Shannon entropy based information measures, the proposed method provides a novel perspective on the relationship between randomness, imprecision, and fuzziness in FBPA.