# Fuzzy operations – Explained with examples

Fuzzy operations are performed on fuzzy sets, whereas crisp operations are performed on crisp sets. Fuzzy operations are very useful in the design of a Fuzzy Logic Controller. It allows the manipulation of fuzzy variables by different means.

### Union:

In the case of the union of crisp sets, we simply have to select repeated elements only once. In the case of fuzzy sets, when there are common elements in both fuzzy sets, we should select the element with the maximum membership value.

The union of two fuzzy sets A and B is a fuzzy set C, written as C = AB

C = AB = {(x, μAB (x)) | ∀x ∈ X}

μC(x) = μAB (x) = μA(x) ∨ μB(x)

= max( μA(x), μB(x) ), ∀x ∈ X

Graphically, we can represent union operations as follows: Red and Blue membership functions represent the fuzzy value for elements in sets A and B, respectively. Wherever these fuzzy functions overlap, we have to consider the point with the maximum membership value.

### Example of Fuzzy Union:

C = AB = {(x, μAB (x)) | ∀x ∈ X}

A = { (x1, 0.2), (x2, 0.5), (x3, 0.6), (x4, 0.8), (x5, 1.0) }

B = { (x1, 0.8), (x2, 0.6), (x3, 0.4), (x4, 0.2), (x5, 0.1) }

μAB (x1) = max( μA(x1), μB(x1) ) = max⁡ { 0.2, 0.8 } = 0.8

μAB (x2) = max( μA(x2), μB(x2) ) = max⁡ { 0.5, 0.6 } = 0.6

μAB (x3) = max( μA(x3), μB(x3) ) = max⁡ { 0.6, 0.4 } = 0.6

μAB (x4) = max( μA(x4), μB(x4) ) = max⁡ { 0.8, 0.2 } = 0.8

μAB (x5) = max( μA(x5), μB(x5) ) = max⁡ { 1.0, 0.1 } = 1.0

So, AB = { (x1, 0.8), (x2, 0.6), (x3, 0.6), (x4, 0.8), (x5, 1.0) }

### Intersection:

In the case of the intersection of crisp sets, we simply have to select common elements from both sets. In the case of fuzzy sets, when there are common elements in both fuzzy sets, we should select the element with minimum membership value.

The intersection of two fuzzy sets A and B is a fuzzy set C, written as C = AB

C = AB = {(x, μAB (x)) | ∀x ∈ X}

μC(x) = μAB (x) = μA(x) ⋀ μB(x)

= min( μA(x), μB(x) ), ∀x ∈ X

Graphically, we can represent the intersection operation as follows: Red and blue membership functions represent the fuzzy value for elements in sets A and B, respectively. Wherever these fuzzy functions overlap, we have to consider the point with the minimum membership value.

### Example of Fuzzy Intersection:

C = AB = {(x, μAB (x)) | ∀x ∈ X}

A = { (x1, 0.2), (x2, 0.5), (x3, 0.6), (x4, 0.8), (x5, 1.0) }

B = { (x1, 0.8), (x2, 0.6), (x3, 0.4), (x4, 0.2), (x5, 0.1) }

μAB (x1) = min( μA(x1), μB(x1) ) = max⁡ { 0.2, 0.8 } = 0.2

μAB (x2) = min( μA(x2), μB(x2) ) = max⁡ { 0.5, 0.6 } = 0.5

μAB (x3) = min( μA(x3), μB(x3) ) = max⁡ { 0.6, 0.4 } = 0.4

μAB (x4) = min( μA(x4), μB(x4) ) = max⁡ { 0.8, 0.2 } = 0.2

μAB (x5) = min( μA(x5), μB(x5) ) = max⁡ { 1.0, 0.1 } = 0.1

So, AB = { (x1, 0.2), (x2, 0.5), (x3, 0.4), (x4, 0.2), (x5, 0.1) }

### Complement:

Fuzzy complement is identical to crisp complement operation. The membership value of every element in the fuzzy set is complemented with respect to 1, i.e. it is subtracted from 1.

The complement of fuzzy set A, denoted by AC, is defined as

AC = {(x, μAC (x)) | ∀x ∈ X}

AC (x) = 1 – μA(x)

### Example of Fuzzy Complement:

AC (x) = 1 – μA(x)

A = { (x1, 0.2), (x2, 0.5), (x3, 0.6), (x4, 0.8), (x5, 1.0) }

AC = { (x1, 0.8), (x2, 0.5), (x3, 0.4), (x4, 0.2), (x5, 0.0) }

AAC = { (x1, 0.8), (x2, 0.5), (x3, 0.6), (x4, 0.8), (x5, 1.0) } ≠ X

AAC = { (x1, 0.2), (x2, 0.5), (x3, 0.4), (x4, 0.2), (x5, 0.0) } ≠ Φ

Unlike crisp sets, fuzzy sets do not hold the law of contradiction and the law of excluded middle.

## Watch on YouTube: Fuzzy operations

For following fuzzy sets, perform union, complement and intersection operations.

A = { (x1, 0.4), (x2, 0.5), (x3, 0.2), (x4, 0.4), (x5, 0.8) }

B = { (x1, 1.0), (x2, 0.3), (x3, 0.5), (x4, 0.7), (x5, 0.1) }

### 12 Responses

2. Parekh Payal says:

Excellent explanation

3. suhail says:

Graphically we can represent union operation as follow. this sentence has a mistake please correct it . it should be “intersection ” in the place of “Union”

• codecrucks says:

Thank you very much for your input. Appreciated and corrected.

4. Alachew Amaneh says:

How to show union is associative in fuzzy set

• codecrucks says:

Its similar to crisp set instead of union, you shall consider max of membership value in fuzzy set.

5. Alachew Amaneh says:

Please tell me how we prove that union is associtive

6. Prasanta Kumar Raut says:

sir please suggest me if the parameter are fuzzy number then how to find minimum weight

• codecrucks says:

Your question is not clear to me. Can you plz rephrase it?