Is 'Fuzzy Theory' an appropriate tool for large scale problems?/ Ranjit Biswas.
Material type:
TextSeries: SpringerBriefs in applied sciences and technology. Computational intelligencePublication details: Switzerland : Springer, ©2016.Description: viii, 64 p. ; illustrationsISBN: - 9783319267173
- 511.313 23 B623
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 511.313 B623 (Browse shelf(Opens below)) | Available | C26530 |
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| 511.309 B812 From Peirce to Skolem : | 511.309 D794 Perspectives on the history of mathematical logic | 511.3092 D272 Logical dilemmas | 511.313 B623 Is 'Fuzzy Theory' an appropriate tool for large scale problems?/ | 511.313 D997 Soft computing in economics and finance/ | 511.313 Ib14 Introduction to applied fuzzy electronics/ | 511.313 In61 Fuzzy logic and applications: proceedings/ |
It includes bibliographical references and abbreviations.
1. Introduction --
2. Two Hidden Facts about Fuzzy Set Theory (and, about any Soft Computing Set Theory) --
3. Cognitive Intuitionistic Fuzzy System (CIFS) --
4. Is?Fuzzy Theory? an Appropriate Tool for Large Size Problems? --
5. Ordering (or Ranking) of Elements in an IFS on the basis of Their Amount of Belongingness --
6. An Application Domain to Understand the Potential of Intuitionistic Fuzzy Theory over Fuzzy Theory --
7. An Example of Application Domain to Understand the Potential of Fuzzy Theory over Intuitionistic Fuzzy Theory in Some Cases --
8. Conclusion --
9. Future Research Directions.
The work in this book is based on philosophical as well as logical views on the subject of decoding the ‘progress’ of decision making process in the cognition system of a decision maker (be it a human or an animal or a bird or any living thing which has a brain) while evaluating the membership value µ(x) in a fuzzy set or in an intuitionistic fuzzy set or in any such soft computing set model or in a crisp set. A new theory is introduced called by “Theory of CIFS”. The following two hypothesis are hidden facts in fuzzy computing or in any soft computing process :-
Fact-1: A decision maker (intelligent agent) can never use or apply ‘fuzzy theory’ or any soft-computing set theory without intuitionistic fuzzy system.
Fact-2 : The Fact-1 does not necessarily require that a fuzzy decision maker (or a crisp ordinary decision maker or a decision maker with any other soft theory models or a decision maker like animal/bird which has brain, etc.) must be aware or knowledgeable about IFS Theory!
The “Theory of CIFS” is developed with a careful analysis unearthing the correctness of these two facts. Two examples of ‘decision making problems’ with complete solutions are presented out of which one example will show the dominance of the application potential of intuitionistic fuzzy set theory over fuzzy set theory, and the other will show the converse i.e. the dominance of the application potential of fuzzy set theory over intuitionistic fuzzy set theory in some cases. The “Theory of CIFS” may be viewed to belong to the subjects : Theory of Intuitionistic Fuzzy Sets, Soft Computing, Artificial Intelligence, etc.
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