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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.
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What is the difference between fuzzy number and crisp number?
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.
Difference between fuzzy set theory and crisp set theory?
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.
What is the difference between classical set theory and fuzzy set theory?
Classical theory is a reference to established theory. Fuzzy set theory is a reference to theories that are not widely accepted.
When you call fuzzy set as fuzzy graph?
fuzzy graph is not a fuzzy set, but it is a fuzzy relation.
What is crisp set?
crisp set is nothing but the set of newly printed money.
What is zadeh's extension principle?
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 demorgan's laws for crisp set theory?
prove the intersction for crisp set theory
What is the meaning of crisp in crisp set?
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.
What is fuzzy function?
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
What is the difference between fuzzy set and L-fuzzy set?
A fuzzy set (class) A in X is characterized by a membership (characteristic)function fA : X--> [0,1] which associates with each point in X a realnumber in the interval [0, 1], with the value of fA(x) at x representingthe "grade of membership" of x in A. Thus, the nearer the value offA(x) to unity, the higher the grade of membership of x in A. When Ais a set in the ordinary sense of the term, its membership function cantake only two values 0 and 1, with fA(x) = 1 or 0 according as xdoes or does not belong to A. Thus, in this case fA(x) reduces to thefamiliar Characteristic function of a set A. (When there is a need todifferentiate between such sets and fuzzy sets, the sets with two-valuedcharacteristic functions will be referred to as ordinary sets or simply sets. )On the other hand , an L-fuzzy set A in X is characterized by the membership function fA :L--> L , where L is a complete lattice with an involutive order preserving operation N : L--> L.
What is 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 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
What is a difference between a collection and a set?
None. A set is a collection and a collection is a set.
