Invited Speaker---Dr. Huchang Liao, Professor
Sichuan University, China
Huchang Liao is a researcher at Sichuan University, China. He has published 3 monographs, 1 chapter, and more than 120 peer-reviewed papers, many in high-quality international journals including EJOR, Omega, IEEE TFS, IEEE TC, KBS, etc. He was ranked top 762 in the ESI top 1% of the world’s 3296 most influential scientists in the field of Computer Science, and top 5203 in the ESI top 1% of the world’s 8834 most influential scientists in the field of Engineering, in January 2019. He is the Senior Member of IEEE since 2017. He is the Associate Editor, Guest Editor or Editorial Board Member for 26 international journals, including Information Fusion (SCI, IF: 6.639), Technological and Economic Development of Economy (SSCI, IF=3.244), IJFS (SCI, IF: 2.396), JIFS(SCI: IF: 1.426).
Artificial Intelligence, Applied Mathematics, Management Science, Information Science, Decision Analysis, Fuzzy Set Theory, Decision Making, Decision Support Systems, Computational Linguistics, Decision Making Under Uncertainty, Computer-Aided Decision Making, etc.
The Extension Principle for Probabilistic Linguistic Term Sets
The probabilistic linguistic term set (PLTS) extends the notion of the linguistic variable (LV). The existing operations of PLTSs are mainly based on the subscripts of linguistic terms and their probabilities while the membership functions of linguistic terms are ignored. Consequently, these operations may cause a loss of information. The extension principle is useful in doing the calculations between LVs and it considers membership functions simultaneously. Inspired by this idea, in this study, we introduce the extension principle for PLTSs and then define the operations of PLTSs. A novel representation of the PLTS is given. Then, the union of PLTSs is presented. Afterwards, we define the extension principle for PLTSs and give the algebraic operations of PLTSs. These algebraic operations based on the extension principle contain both the probabilities and membership functions of linguistic terms, and thus can enhance the precision of final results.