Proving A = B if A -> B and B -> A and A and B are Fuzzy sets

  • Thread starter rmcdra
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In summary, if A and B are non-empty fuzzy sets in the universe of discourse X, and A and B are not classical sets, then A and B cannot be proven to be equal solely based on the fact that A is a subset of B and B is a subset of A. This is because the evaluation of (A' \cap B)(x) = min(1-A(x), B(x)) is equal to 0 only when A(x) = B(x) = 1, indicating that A and B are classical sets. However, this contradicts the given hypothesis that A and B are not classical sets. Therefore, A and B are not necessarily equal if A \subseteq B and B \subseteq A and A and
  • #1
rmcdra
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Need help proving A != B if A -> B and B -> A and A and B are Fuzzy sets

Homework Statement



Let X be the universe of discourse where x [tex]\in[/tex] X. Let A and B be non-empty fuzzy sets in X. If A and B are not classical sets then A [tex]\subseteq[/tex] B and B [tex]\subseteq[/tex] A is not sufficient enough to show that A = B.

This is what I'm trying to solve I think I wrote it correctly but if it needs rewording please let me know.

Homework Equations



If A [tex]\subseteq[/tex] B then A(x) [tex]\leq[/tex] B(x)
A'(x) = 1 - A(x)
(A [tex]\cap[/tex] B)(x) = min{A(x), B(x)}

The Attempt at a Solution


Proof: Assume A = B. This means that A and B share every element in common. If A = B then the evaluation of (A' [tex]\cap[/tex] B)(x) = min(1-A(x), B(x)) = 0. But min(1-A(x), B(x)) = 0 only when for all x such that, A(x) = B(x) = 1. This means A and B are classical sets but it is established by the hypothesis that A and B are not classical sets. So A [tex]\neq[/tex] B if A [tex]\subseteq[/tex] B and B [tex]\subseteq[/tex] A and A and B are not classical sets.

This is what I reasoned but I'm not feeling confident about this if I'm going about it the right way. If there is anything wrong with it I would really like to know.
 
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  • #2
Exactly what definition of "fuzzy sets" are you using? There are several equivalent definitions and exactly how you prove something like this depends upon which you are using.


Also note that your title, "A != B if A -> B and B -> A" is NOT the same as "A [itex]\subseteq[/itex] and BA [itex]\subseteq[/itex] A is not sufficient to prove A= B".
 
  • #3
I am using the definiton of fuzzy sets as defined by Zadeh as being generalizations of classical sets where membership and non-membership is not clearly defined.
 

1. What is the definition of a fuzzy set?

A fuzzy set is a mathematical concept that allows for the representation of uncertainty and partial truth in a set. It is a generalization of a traditional set, where an element can belong to the set with a degree of membership between 0 and 1.

2. How do you prove that A equals B if A implies B and B implies A?

In order to prove that A equals B in fuzzy sets, we need to show that the degree of membership of each element in set A is equal to the degree of membership of the same element in set B. This can be done by using the properties of implication and logical operators, such as conjunction and disjunction, to show that the membership functions of both sets are equivalent.

3. What is the significance of A and B being fuzzy sets?

The fuzziness of A and B means that the membership of an element in either set can be represented by a range of values, rather than a binary 0 or 1. This allows for a more nuanced understanding of the relationship between the two sets, as well as the ability to handle incomplete or uncertain data.

4. Can you provide an example of proving A equals B in fuzzy sets?

Sure, let's say we have two fuzzy sets, A and B, where A = {0.2/3, 0.5/5, 0.8/7} and B = {0.2/3, 0.5/5, 0.8/7}. By using the properties of implication and logical operators, we can show that the membership function for each element in set A is equal to the membership function for the same element in set B. Therefore, A equals B in fuzzy sets.

5. What are the applications of fuzzy sets in science?

Fuzzy sets have a wide range of applications in science, including data analysis, pattern recognition, decision making, and control systems. They can be used to handle imprecise or uncertain data, and can provide a more accurate representation of real-world phenomena that are not easily defined by traditional sets.

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