Some concepts and methods of information granule diffusion

When a set of information granules is incomplete to represent an object A, it seems that we could employ the information diffusion techniques to change the granules into fuzzy granulations for filling the gaps with respect to a scarcity. In this paper, we suggest the concept of information granule diffusion, and give the 1-dimension linear diffusion model and normal diffusion model. We give two examples in engineering to show that the methods of information granule diffusion would play a pivotal role in the processing of incomplete, uncertain, vague information.

[1]  Settimo Termini,et al.  A Definition of a Nonprobabilistic Entropy in the Setting of Fuzzy Sets Theory , 1972, Inf. Control..

[2]  Asaf Degani,et al.  Procedures in complex systems: the airline cockpit , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[3]  Huang Chong-fu,et al.  Principle of information diffusion , 1997 .

[4]  Andrzej Skowron,et al.  Adaptive Decision-Making by Systems of Cooperating Intelligent Agents Organized on Rough Mereological Principles , 1996, Intell. Autom. Soft Comput..

[5]  A. Bonaert Introduction to the theory of Fuzzy subsets , 1977, Proceedings of the IEEE.

[6]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[7]  Andrzej Skowron,et al.  Rough Sets and Information Granulation , 2003, IFSA.

[8]  Wang Xin A Model Based on the Theory of Fuzzy Information Distribution for Selecting Coal Mining Method , 2000 .

[9]  Li Zhang,et al.  Unlimited information diffusion method and application in risk analysis in coronary heart disease , 2004, Int. J. Gen. Syst..

[10]  Chongfu Huang,et al.  Principle of information diffusion , 1997, Fuzzy Sets Syst..

[11]  Lotfi A. Zadeh,et al.  Fuzzy sets and information granularity , 1996 .

[12]  Claudio Moraga,et al.  A diffusion-neural-network for learning from small samples , 2004, Int. J. Approx. Reason..

[13]  T.Y. Lin Sets with partial memberships: a rough set view of fuzzy sets , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[14]  Karl Rihaczek,et al.  1. WHAT IS DATA MINING? , 2019, Data Mining for the Social Sciences.

[15]  Huang Chong-fu,et al.  Demonstration of benefit of information distribution for probability estimation , 2000 .

[16]  A. Kaufman,et al.  Introduction to the Theory of Fuzzy Subsets. , 1977 .

[17]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[18]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[19]  Lech Polkowski Rough Mereology: A Survey of New Developments with Applications to Granular Computing, Spatial Reasoning and Computing with Words , 2003, RSFDGrC.

[20]  Claudio Moraga,et al.  Extracting fuzzy if-then rules by using the information matrix technique , 2005, J. Comput. Syst. Sci..

[21]  Donald H. Kraft,et al.  Fuzzy information systems: managing uncertainty in databases and information retrieval systems , 1997, Fuzzy Sets Syst..

[22]  Claudio Moraga,et al.  A Fuzzy Risk Model and Its Matrix Algorithm , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[23]  M. Mead,et al.  Cybernetics , 1953, The Yale Journal of Biology and Medicine.

[24]  E. Parzen On Estimation of a Probability Density Function and Mode , 1962 .

[25]  Rory A. Fisher,et al.  Theory of Statistical Estimation , 1925, Mathematical Proceedings of the Cambridge Philosophical Society.

[26]  Huang Chong-fu An application of calculated fuzzy risk , 2002 .

[27]  Andrzej Skowron,et al.  Approximation Spaces and Information Granulation , 2004, Trans. Rough Sets.

[28]  Yiyu Yao,et al.  A Partition Model of Granular Computing , 2004, Trans. Rough Sets.

[29]  Yangsheng You,et al.  An approach to calculate optimal window-width serving for the information diffusion technique , 2004, Int. J. Gen. Syst..

[30]  Yong Shi,et al.  Towards Efficient Fuzzy Information Processing - Using the Principle of Information Diffusion , 2002, Studies in Fuzziness and Soft Computing.

[31]  Yee Leung,et al.  A NEW ALGORITHM FOR ESTIMATING THE RISK OF NATURAL DISASTERS WITH INCOMPLETE DATA , 2000 .

[32]  Chongfu Huang An application of calculated fuzzy risk , 2002, Inf. Sci..

[33]  A. P. Bonaert,et al.  Introduction to the Theory of Fuzzy Subsets-vol. 1: Fundamental Theoretical Elements , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[34]  L. Zadeh,et al.  Data mining, rough sets and granular computing , 2002 .