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In short, for a crisp set (subset) elements of the set definitely do belong to the set, while in a fuzzy set (subset) elements of the set have a degree of membership in the set. To make things clearer:

Suppose we have a reference set X={x_1, ...} and a subset Y={y_1, ...} of X. If Y represents a crisp subset of X, then for all x_n belonging to X, x_n either belongs or Y or does not belong to Y. We can write this by assigning a function C which takes each member of X to 1 iff it belongs to Y, and 0 iff it does not belong to Y. E. G. Suppose we have the set {1, 2, 3, 4, 5}. For the crisp subset {1, 2, 4} we could write this in terms of a function C which takes 1 to 1, 2 to 1, 3 to 0, 4 to 1, and 5 to 0, or we can write {(1, 1), (2, 1), (3, 0), (4, 1), (5, 1)}.

For a fuzzy subset F of a reference set X the elements of F may belong to F to a degree in between 0 and 1 (as well as may belong to F to degree 0 or 1). We can write this by assigning a function M which takes each member of X to a number in the interval of real numbers from 0 to 1, [0, 1] to represent its degree of membership. Here "larger" numbers represent a greater degree of membership in the fuzzy subset F. For example, for the reference set {1, 2, 3, 4, 5} we could have a function M which takes 1 to .4, 2 to 1, 3 to .6, 4 to .2, and 5 to 0, or {(1, .4), (2, 1), (3, .6), (4, .2), (5, 0)}, with 3 having a greater degree of membership in F than 4 does, since .6>.2.

Q: Different between crisp set and fuzzy set?

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The fundamental difference is that in fuzzy set theory permits the gradual assessment of the membership of elements in a set and this is described with the aid of a membership function valued in the real unit interval [0, 1]. Better, the degree of membership of the elements of a set can take values ranging between 0 and 1 allowing for a ranking of membership. Conversely, crisp set theory is a classical bivalent set so that the membership function only takes values 0 or 1. In this case, one can know only if an element of the set have or not a particular characteristic and a ranking of membership is not possible.

Classical theory is a reference to established theory. Fuzzy set theory is a reference to theories that are not widely accepted.

prove the intersction for crisp set theory

A null set, a finite set, a countable infinite set and an uncountably infinite set.

method in wrinting a set

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Each crisp number is a single point.example 3 or 5.5 or6.But each fuzzy number is a fuzzy set with different degree of closeness to a given crisp number example,about 3,nearly 5 and a half,almost 6.

Let A be a crisp set defined over the universe X. Then for any element x in X,either x is a member of A or not.In a fuzzy set,it is not necessary that x is the full member of the set or not a member. It can be the partial member of the set.

The fundamental difference is that in fuzzy set theory permits the gradual assessment of the membership of elements in a set and this is described with the aid of a membership function valued in the real unit interval [0, 1]. Better, the degree of membership of the elements of a set can take values ranging between 0 and 1 allowing for a ranking of membership. Conversely, crisp set theory is a classical bivalent set so that the membership function only takes values 0 or 1. In this case, one can know only if an element of the set have or not a particular characteristic and a ranking of membership is not possible.

fuzzy graph is not a fuzzy set, but it is a fuzzy relation.

Classical theory is a reference to established theory. Fuzzy set theory is a reference to theories that are not widely accepted.

crisp set is nothing but the set of newly printed money.

 Fuzzy inference is a computer paradigm based on fuzzy set theory, fuzzy if-then- rules and fuzzy reasoning  Applications: data classification, decision analysis, expert systems, times series predictions, robotics & pattern recognition  Different names; fuzzy rule-based system, fuzzy model, fuzzy associative memory, fuzzy logic controller & fuzzy system Fuzzy inference is a computer paradigm based on fuzzy set theory, fuzzy if-then- rules and fuzzy reasoning  Applications: data classification, decision analysis, expert systems, times series predictions, robotics & pattern recognition  Different names; fuzzy rule-based system, fuzzy model, fuzzy associative memory, fuzzy logic controller & fuzzy system

The extension principle is a basic concept in the fuzzy set theory that extends crisp domains of mathematical expressions to fuzzy domains. Suppose f(.) is a function from X to Y and A is a fuzzy set on X defined as: A=ma(x1)/x1 + ma(x2)/x2 + ...... + ma(xn)/xn Where ma is the Membership Function of A. the + sign is a fuzzy OR (Max) and the / sign is a notation (indicated the variable xi in discourse domain X - NOT DIVISION) Then the extension principle states that the image of fuzzy set A under the mapping f(.) can be expressed as a fuzzy set B, B=f(A)=ma(x1)/y1 + ma(x2)/y2 + ...... + ma(xn)/yn where yi = f(xi) , i = 1,2,3,....,n

prove the intersction for crisp set theory

Crisp sets are the sets that we have used most of our life. In a crisp set, an element is either a member of the set or not. For example, a jelly bean belongs in the class of food known as candy. Mashed potatoes do not.

membership function is the one of the fuzzy function which is used to develope the fuzzy set value . the fuzzy logic is depends upon membership function

computer-based system(it contains both hard ware and software) that can process data that are incomplete or only partially correct Fuzzy logic was introduced as an artificial intelligence technique, when it was realized that normal boolean logic would not suffice. When we make intelligent decisions, we cannot limit ourselves to "true" or "false" possibilities (boolean). We have decisions like "maybe" and other shades of gray. This is what is introduced with fuzzy logic: the ability to describe degrees of truth. Example: in fuel station if you stop a fuel injecting motor at 1.55897ltrs.it can be done with the help of fuzzy logic.fuzzy has a meaning like accurate.