Soft Computing Fuzzy Propositions Prof. Debasis Samanta Department of Computer Science & Engineering IIT Kharagpur
1
Fuzzy Propositions • Two-valued logic vs. Multi-valued logic • Examples of Fuzzy proposition • Fuzzy proposition vs. Crisp proposition • Canonical representation of Fuzzy proposition • Graphical interpretation of Fuzzy proposition
Debasis Samanta CSE IIT Kharagpur
2
Two-valued logic vs. Multi-valued logic • The basic assumption upon which crisp logic is based - that every proposition is either TRUE or FALSE. • The classical two-valued logic can be extended to multi-valued logic. • As an example, three valued logic to denote true(1), false(0) and indeterminacy ( 1/2 ).
Debasis Samanta CSE IIT Kharagpur
3
Two-valued logic vs. Multi-valued logic Different operations with three-valued logic can be extended as shown in the truth table: a b 0
0
0
0
1
1
1
0
½
0
½
1
1
½
0
1
0
1
1
1
0
½
0
0
½
½
½
½
½
½
½
½
½
½
1
½
1
½
1
½
1
½
1
0
0
1
0
0
0
1
½
½
1
0
½
½
1
1
1
1
0
1
1 Debasis Samanta CSE IIT Kharagpur
4
Three-valued logic Fuzzy connectives defined for such a three-valued logic better can be stated as follows: Symbol
: Absolutely false : Partially false : May be false or not false : May be true or not true : Partially true : Absolutely true.
Debasis Samanta CSE IIT Kharagpur
6
Fuzzy proposition: Example 2
Debasis Samanta CSE IIT Kharagpur
7
Fuzzy proposition: Example 2
Debasis Samanta CSE IIT Kharagpur
8
Fuzzy proposition vs. Crisp proposition • The fundamental difference between crisp (classical) proposition and fuzzy propositions is in the range of their truth values. • While each classical proposition is required to be either true or false, the truth or falsity of fuzzy proposition is a matter of degree. • The degree of truth of each fuzzy proposition is expressed by a value in the interval [0,1] both inclusive. Debasis Samanta CSE IIT Kharagpur
9
Canonical representation of Fuzzy proposition
Debasis Samanta CSE IIT Kharagpur
10
Canonical representation of Fuzzy proposition
Debasis Samanta CSE IIT Kharagpur
11
Graphical interpretation of fuzzy proposition
Debasis Samanta CSE IIT Kharagpur
12
Fuzzy system
Debasis Samanta CSE IIT Kharagpur
13
Thank You!! Debasis Samanta CSE IIT Kharagpur
14
Soft Computing Fuzzy Implication Prof. Debasis Samanta Department of Computer Science & Engineering IIT Kharagpur
1
Fuzzy implications • Fuzzy rule • Examples of fuzzy implications • Interpretation of fuzzy rules • Product operators • Zadeh’s Max-Min rule and some examples
Debasis Samanta CSE IIT Kharagpur
2
Fuzzy rule
Debasis Samanta CSE IIT Kharagpur
3
Fuzzy implication : Example 1
Debasis Samanta CSE IIT Kharagpur
4
Fuzzy implication : Example 2
Debasis Samanta CSE IIT Kharagpur
5
Fuzzy implication : Example 2
Debasis Samanta CSE IIT Kharagpur
6
Interpretation of fuzzy rules
Debasis Samanta CSE IIT Kharagpur
7
Interpretation as A coupled with B
Debasis Samanta CSE IIT Kharagpur
8
Interpretation as A coupled with B
Debasis Samanta CSE IIT Kharagpur
9
Interpretation as A coupled with B
Debasis Samanta CSE IIT Kharagpur
10
Product Operators
Debasis Samanta CSE IIT Kharagpur
11
Interpretation of A entails B
Debasis Samanta CSE IIT Kharagpur
12
Interpretation of A entails B
Debasis Samanta CSE IIT Kharagpur
13
Interpretation of A entails B
Debasis Samanta CSE IIT Kharagpur
14
Example 3: Zadeh’s Max-Min rule
Debasis Samanta CSE IIT Kharagpur
15
Example 3: Zadeh’s Max-Min rule
Debasis Samanta CSE IIT Kharagpur
16
Example 3: Zadeh’s Max-Min rule
Debasis Samanta CSE IIT Kharagpur
17
Example 4:
Debasis Samanta CSE IIT Kharagpur
18
Example 4:
Debasis Samanta CSE IIT Kharagpur
19
Example 4:
Debasis Samanta CSE IIT Kharagpur
20
Example 4:
Debasis Samanta CSE IIT Kharagpur
21
Example 4:
Debasis Samanta CSE IIT Kharagpur
22
Thank You! 23
Soft Computing Fuzzy Inferences Debasis Samanta Department of Computer Science and Engineering IIT KHARAGPUR
Fuzzy inferences
An example from propositional logic
Inferring procedures in Fuzzy logic
Fuzzy inferring procedures
Generalized Modus Ponens : Example
Example: Generalized Modus Ponens
Generalized Modus Ponens
Example. Generalized Modus Ponens
Example. Generalized Modus Ponens
Example. Generalized Modus Tollens
Example. Generalized Modus Tollens
Example. Generalized Modus Tollens
Practical example
Practice
Soft Computing Defuzzyfication Techniques-I Debasis Samanta Department of Computer Science and Engineering IIT KHARAGPUR
1
What is defuzzification?
Debasis Samanta CSE IIT Kharagpur
2
Example 2. Fuzzy to crisp As an another example, let us consider a fuzzy set whose membership function is shown in the following figure.
What is the crisp value of the fuzzy set in this case? Debasis Samanta CSE IIT Kharagpur
3
Example 3. Fuzzy to crisp
Debasis Samanta CSE IIT Kharagpur
4
Why defuzzification?
Debasis Samanta CSE IIT Kharagpur
5
Generic structure of a Fuzzy system Following figure shows a general framework of a fuzzy system.
Debasis Samanta CSE IIT Kharagpur
6
Defuzzification Techniques
Debasis Samanta CSE IIT Kharagpur
7
Defuzzification methods A number of defuzzification methods are known. Such as 1)
Lambda-cut method
2)
Weighted average method
3)
Maxima methods
4)
Centroid methods
Debasis Samanta CSE IIT Kharagpur
8
Lambda-cut method
Debasis Samanta CSE IIT Kharagpur
9
Lambda-cut method Lambda-cut method is applicable to derive crisp value of a fuzzy set or relation. • Lambda-cut method for fuzzy relation The same has been applied to Fuzzy set • Lambda-cut method for fuzzy set
In many literature, Lambda-cut method is also alternatively termed as Alpha-cut method.
Debasis Samanta CSE IIT Kharagpur
10
Lamda-cut method for fuzzy set
Debasis Samanta CSE IIT Kharagpur
11
Lambda-cut for a fuzzy set : Example
Debasis Samanta CSE IIT Kharagpur
12
Lambda-cut sets : Example Two fuzzy sets P and Q are defined on x as follows.
P
0.1
0.2
0.7
0.5
0.4
Q
0.9
0.6
0.3
0.2
0.8
Debasis Samanta CSE IIT Kharagpur
13
Lambda-cut for a fuzzy relation
Debasis Samanta CSE IIT Kharagpur
14
Debasis Samanta CSE IIT Kharagpur
15
Some properties of -cut relations
Debasis Samanta CSE IIT Kharagpur
16
Summary: Lambda-cut methods
Lambda-cut method converts a fuzzy set (or a fuzzy relation) into a crisp set (or relation).
Debasis Samanta CSE IIT Kharagpur
17
Output of a Fuzzy System
Debasis Samanta CSE IIT Kharagpur
18
Output of a fuzzy System
Debasis Samanta CSE IIT Kharagpur
19
Output fuzzy set : Illustration
For instance, let us consider the following:
Debasis Samanta CSE IIT Kharagpur
20
Output fuzzy set : Illustration
Debasis Samanta CSE IIT Kharagpur
21
Output fuzzy set : Illustration
Debasis Samanta CSE IIT Kharagpur
22
Output fuzzy set : Illustration
Debasis Samanta CSE IIT Kharagpur
23
Thank You!! 24
Soft Computing Defuzzyfication Techniques-II Debasis Samanta Department of Computer Science and Engineering IIT KHARAGPUR
Page 2 of 33. 2. ASIMAVA ROY CHOUDHURY. MECHANICAL ENGINEERING. IIT KHARAGPUR. A cutting tool is susceptible to breakage, dulling and wear. TOOL WEAR AND TOOL LIFE. Rake. surface. Pr. flank. Aux. flank. Page 2 of 33. Page 3 of 33. 3. ASIMAVA ROY CHOU
There was a problem previewing this document. Retrying... Download. Connect more apps... Try one of the apps below to open or edit this item. Week 1 Lecture Material.pdf. Week 1 Lecture Material.pdf. Open. Extract. Open with. Sign In. Main menu.
All Day Breakfast. Meat or Vegetarian. Lasagne ... Apple Pie with custard. Winter Berry Sponge with custard. Chocolate Krispie. WEEK 2. 11th Sept, 2nd Oct, ...
On Monday mornings we will sing the National Anthem and do birthday announcements and pencils and. for the following days the students will be asked to ...
Two Types of Variables: ⢠Local Variables. â Declared inside of a funcion. â Exist only within that funcion. ⢠Global Variables. â Declared outside of all funcfions.
where t. G is another Hilbert space. The dimension of this Hilbert space is either 0 or 1. This is so because the Hilbert space t. G must be spanned by the single.
Page 4 of 8. Page 4 of 8. 2-TLC-Lecture note.pdf. 2-TLC-Lecture note.pdf. Open. Extract. Open with. Sign In. Main menu. Displaying 2-TLC-Lecture note.pdf.
Retrying... Download. Connect more apps... Try one of the apps below to open or edit this item. phys570-lecture-2.pdf. phys570-lecture-2.pdf. Open. Extract.
the natural metric, i.e. notion of length, for a random variable its standard deviation,. ( )2. = E t t x x. (5.1) with covariance as the associated notion of inner product ...
Continuous Variables. - Cumulative probability function. PDF has dimensions of x-1. Expectation value. Moments. Characteristic function generates moments: .... from realized sample, parameters are unknown and described probabilistically. Parameters a
Old Dominion University. Department of ... Our Hello World! [user@host ~]$ python .... maxnum = num print("The biggest number is: {}".format(maxnum)) ...
Oct 23, 2017 - Not a trivial issue: input-output linkages, firm-to-firm trade relationships, etc. ⢠ACF doesn't work in this case. ⢠Recall that mit = m(kit,lit,Ïit). ⢠If mit is directly an input factor in gross production function, which var
18. Metric. Formula. Average classifica on accuracy ... Precision: Frac on of retrieved instances that are relevant ... What does the precision score of 1.0 mean?
11/15/2009 - Week 2 Revelation 1:4-20. Following the Message: 1. vs. 4 â âWho is, who was, and who is to comeâ is a is a description for us to indicate that He is ...
