By Ranjit Biswas
The paintings during this publication relies on philosophical in addition to logical perspectives with reference to interpreting the ‘progress’ of selection making procedure within the cognition approach of a call maker (be it a human or an animal or a poultry or any residing factor which has a mind) whereas comparing the club worth µ(x) in a fuzzy set or in an intuitionistic fuzzy set or in this kind of gentle computing set version or in a crisp set. a brand new conception is brought known as via “Theory of CIFS”. the next speculation are hidden evidence in fuzzy computing or in any delicate computing approach :-
Fact-1: a call maker (intelligent agent) can by no means use or follow ‘fuzzy concept’ or any soft-computing set idea with no intuitionistic fuzzy system.
Fact-2 : The Fact-1 doesn't unavoidably require fuzzy determination maker (or a crisp usual selection maker or a call maker with the other tender concept versions or a call maker like animal/bird which has mind, etc.) needs to be conscious or acquainted with IFS Theory!
The “Theory of CIFS” is built with a cautious research unearthing the correctness of those proof. examples of ‘decision making difficulties’ with entire ideas are awarded out of which one instance will exhibit the dominance of the applying power of intuitionistic fuzzy set thought over fuzzy set idea, and the opposite will convey the communicate i.e. the dominance of the applying strength of fuzzy set conception over intuitionistic fuzzy set conception sometimes. The “Theory of CIFS” might be seen to belong to the topics : thought of Intuitionistic Fuzzy units, smooth Computing, man made Intelligence, etc.
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Additional resources for Is ‘Fuzzy Theory’ an Appropriate Tool for Large Size Problems?
E. the hesitation component or undecided component) h(x, t) to be nil for the element x of the universe X, while going to propose the corresponding membership value μ(x). Proof Suppose that APT(x) = T. Consider the 2-D curve m = m(x, t) on tm-plane (as shown in Fig. 12). Suppose that Am is the area under the curve m = m(x, t) bounded by the lines t-axis, m-axis and the line t = T. Clearly, corresponding to a given decision maker Am is not a function of t but of the parameter x. Now consider the 2-D curve n = n(x, t) on tn-plane (as shown in Fig.
But CESFM in its theory does not exclude penalty kicks by each team as followed presently after 90 min of play. The result of penalty kicks is one of the many components in the method of CESFM. This proposed fuzzy method can easily extract the true dominant team out of the two tied teams using a software called by CESFM-software with the help of continuous real time inputs from the Referee where some of the inputs are given by the Referee using his ‘fuzzy pocket machine’ M which is an electronic wireless machine (looking like a mobile phone).
D can recognize any element of any of the above N sets but up to certain extent; for completely unknown object it is 0 %, for a completely sure case it is 100 %, and for any other case with some doubt, it is in the range of open interval (0 %, 100 %). Our main interest here is NOT JUST to understand: “How does Mr. ” Pattern Recognition or Object Recognition is one of the earliest and important problems to the earth. We need to recognize new diseases, new stars, new planets, new solar systems, new viruses, new bacteria, etc.
Is ‘Fuzzy Theory’ an Appropriate Tool for Large Size Problems? by Ranjit Biswas