From Fuzzy Models to Granular Fuzzy Models

详细信息    查看全文

• 作者：Witold Pedrycz (12) pedrycz@ee.ualberta.ca
• 关键词：information granularity &#8211 ; granular fuzzy models &#8211 ; principle of justifiable granularity &#8211 ; knowledge management &#8211 ; granularity allocation
• 刊名：Lecture Notes in Computer Science
• 出版年：2011
• 出版时间：2011
• 年：2011
• 卷：6857
• 期：1
• 页码：75-82
• 全文大小：190.0 KB
• 参考文献：1. Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction. Kluwer Academic Publishers, Dordrecht (2003)
  2. Bargiela, A., Pedrycz, W. (eds.): Human-Centric Information Processing Through Granular Modelling. Springer, Heidelberg (2009)
  3. Bargiela, A., Pedrycz, W.: Toward a theory of Granular Computing for human-centered information processing. IEEE Transactions on Fuzzy Systems 16(2), 320–330 (2008)
  4. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, N. York (1981)
  5. Pedrycz, W., Gomide, F.: Fuzzy Systems Engineering: Toward Human-Centric Computing. John Wiley, Hoboken (2007)
  6. Pedrycz, W., Song, M.: Analytic Hierarchy Process (AHP) in group decision making and its optimization with an allocation of information granularity. IEEE Trans. on Fuzzy Systems (to appear 2011)
  7. Zadeh, L.A.: Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems 90, 111–117 (1997)
• 作者单位：1. Department of Electrical & Computer Engineering, University of Alberta, Edmonton, Canada2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Fuzzy models occupy one of the dominant positions on the research agenda of fuzzy sets exhibiting a wealth of conceptual developments and algorithmic pursuits as well as a plethora of applications. Granular fuzzy modeling dwelling on the principles of fuzzy modeling opens new horizons of investigations and augments the existing design methodology exploited in fuzzy modeling. In a nutshell, granular fuzzy models are constructs built upon fuzzy models or a family of fuzzy models. We elaborate on a number of compelling reasons behind the emergence of granular fuzzy modelling, and granular modeling, in general. Information granularity present in such models plays an important role. Given a fuzzy model M, the associated granular model incorporates granular information to quantify a performance of the original model, facilitate collaborative pursuits of knowledge management and knowledge transfer. We discuss several main categories of granular fuzzy models where such categories depend upon the formalism of information granularity giving rise to interval-valued fuzzy models, fuzzy fuzzy model (fuzzy2 models, for short), and rough -fuzzy models. The design of granular fuzzy models builds upon two fundamental concepts of Granular Computing: the principle of justifiable granularity and an optimal allocation (distribution) of information granularity. The first one supports a construction of information granules of a granular fuzzy model. The second one emphasizes the role of information granularity being treated as an important design asset. The underlying performance indexes guiding the design of granular fuzzy models are discussed and a multiobjective nature of the construction of these models is stressed.