**Fuzzy Logic:** Fuzzy Logic is the logic underlying approximate rather than exact. It is an extension of multivalued logic: Everything, including truth, is a matter of degree. It contains as special cases not only the classical two-value logic and multivalue logic system but also probabilistic logic.

A proportion **p **has a **truth value**

- 0 or 1 in the two-value system.
- element of set T in the multivalue system.
**The range over the fuzzy subset of T**in fuzzy logic

**Boolean Logic V/S Fuzzy Logic**

Boolean logic is basically the combination of **0** or **1 **and Boolean logic also uses sharp distinct whereas Fuzzy logic reflects how people think. Fuzzy logic is a set of mathematical principle for knowledge representation and reasoning based on the degree of membership.

As from image above it can be seen that **Boolean** **Logic** can only be **True** or **False** whereas **Fuzzy** **Logic** can be between the values of **True** or **False.**

**Need for Fuzzy Logic**

- Fuzzy Logic is based on intuition and judgment.
- No need for a mathematical model.
- It provides a smooth transition between members and nonmembers.
- Relatively simple, fast and adaptive.
- Less sensitive to system fluctuations.
- Can implement design objectives, difficult to express mathematically, in linguistic or descriptive rules.

**Crisp Set**

Conventional or crisp sets are Binary. An element either belongs to the set or does not.

**{True / False}**

**{1,0}**

In Fuzzy ML code we will change the Crisp value to linguistic words by Fuizzification and then output changed into Crisp value for output by Defuzzification.

**Example of Fuzzification:**

Assume we want to evaluate the health of a person based on his height and weight. The input variables are the crisp values of a person’s height and weight. **Fuzzification **is the process by which the numbers are changed into linguistic words.

**DEFUZZIFICATION**

Defuzzification id a mapping process from a space of fuzzy control actions defined over an output universe of discourse into a space of crisp control actions. It is a process of converting the output fuzzy variable into a unique number.

It has the capability to reduce a fuzzy set into a crisp single-values quantity or into a crisp set; to convert a fuzzy matrix into a crisp matrix, or to convert a fuzzy member into crisp member.

**Methods of Defuzzification:**

- Max-membership principle.
- Centroid method.
- Weighted average method.
- Mean-max membership.
- Center of sums.
- Center of largest area.
- First of maxima, last of maxima.

**Fuzzy Logic Code to Calculate CGPA based on marks of Continuos Assesment, Mid-Term exams and End-Term exams.**

!pip install scikit-fuzzy #install fuzzy in your system #Antecedent is used for input parameters and Consequent is used for output. Here ca,mte,ete are input and cgpa is output. ca=ctrl.Antecedent(np.arange(0,26,1),'ca') #ca marks range 0-25 mte=ctrl.Antecedent(np.arange(0,26,1),'mte') #mte marks range 0-25 ete=ctrl.Antecedent(np.arange(0,51,1),'ete') #ete marks range 0-50 cgpa=ctrl.Consequent(np.arange(0,11,1),'cgpa') #cgpa between 0-10 #now we consider bad ca when marks are between 0-10, average when marks between 9-20 and good when marks between 18-25. ca['bad']=fuzzy.trimf(ca.universe,[0,5,10]) ca['avg']=fuzzy.trimf(ca.universe,[9,15,20]) ca['good']=fuzzy.trimf(ca.universe,[18,23,25]) #now we consider bad mte when marks are between 0-10, average when marks between 9-20 and good when marks between 18-25. mte['bad']=fuzzy.trimf(mte.universe,[0,5,10]) mte['avg']=fuzzy.trimf(mte.universe,[8,15,20]) mte['good']=fuzzy.trimf(mte.universe,[18,23,25]) #now we consider bad ete when marks are between 0-10, average when marks between 18-35 and good when marks between 33-50. ete['bad']=fuzzy.trimf(ete.universe,[0,10,20]) ete['avg']=fuzzy.trimf(ete.universe,[18,25,35]) ete['good']=fuzzy.trimf(ete.universe,[33,40,50]) #now we consider bad cgpa when marks are between 0-5, average when marks between 4-7.5 and good when marks between 7-10. cgpa['bad']=fuzzy.trimf(cgpa.universe,[0,3,5]) cgpa['avg']=fuzzy.trimf(cgpa.universe,[4,6,7.5]) cgpa['good']=fuzzy.trimf(cgpa.universe,[7,9,10]) ca.view() mte.view() ete.view() cgpa.view()

#now we will decide rules based on creteria of ca, mte and ete. rule1=ctrl.Rule(ca['bad'] & mte['bad'] & ete['bad'],cgpa['bad']) rule2=ctrl.Rule(ca['avg'] & mte['avg'] & ete['avg'],cgpa['avg']) rule3=ctrl.Rule(ca['good'] & mte['good'] & ete['good'],cgpa['good']) rule4=ctrl.Rule(ca['bad'] & mte['good'] & ete['good'],cgpa['good']) rule5=ctrl.Rule(ca['good'] & mte['bad'] & ete['good'],cgpa['avg']) rule6=ctrl.Rule(ca['good'] & mte['good'] & ete['bad'],cgpa['bad']) rule7=ctrl.Rule(ca['bad'] & mte['bad'] & ete['good'],cgpa['bad']) rule8=ctrl.Rule(ca['good'] & mte['avg'] & ete['good'],cgpa['good']) rule9=ctrl.Rule(ca['avg'] & mte['good'] & ete['good'],cgpa['good']) rule10=ctrl.Rule(ca['good'] & mte['bad'] & ete['bad'],cgpa['bad'])

#pass the value to ControlSystem and Simulate before calculating actual output. cg_calc=ctrl.ControlSystem([rule1,rule2,rule3,rule4,rule5,rule6,rule7,rule8,rule9,rule10]) cgpaa=ctrl.ControlSystemSimulation(cg_calc) #Now pass input as cgpaa.input['ca']=18 cgpaa.input['mte']=22 cgpaa.input['ete']=41 cgpaa.compute() #calculate cgpa print(cgpaa.output['cgpa']) #print calculated cgpa cgpa.view(sim=cgpaa) #visualize output

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