Your SlideShare is downloading. ×
  • Like
L15  fuzzy logic
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Now you can save presentations on your phone or tablet

Available for both IPhone and Android

Text the download link to your phone

Standard text messaging rates apply

L15 fuzzy logic

  • 212 views
Published

 

Published in Technology , Business
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
212
On SlideShare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
27
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. Fuzzy LogicFuzzy Logic 1Rushdi Shams, Lecturer, Dept of CSE, KUET, Bangladesh
  • 2. Introduction  Form of multivalued logic  Deals reasoning that is approximate rather than precise  the fuzzy logic variables may have a membership value of not only 0 or 1 – that is, the degree of truth of a statement can range between 0 and 1 and is not constrained to the two truth values of classic propositional logic Rushdi Shams, Lecturer, Dept of CSE, KUET, Bangladesh 2
  • 3. Introduction  Fuzzy logic has been applied to many fields, from control theory to artificial intelligence  it still remains controversial among most statisticians, who prefer Bayesian logic, and  some control engineers, who prefer traditional two-valued logic. Rushdi Shams, Lecturer, Dept of CSE, KUET, Bangladesh 3
  • 4. Degrees of truth  let a 100 ml glass contain 30 ml of water.  Then we may consider two concepts: Empty and Full.  The meaning of each of them can be represented by a certain fuzzy set.  Then one might define the glass as being 0.7 empty and 0.3 full.  The concept of emptiness would be subjective and thus would depend on the observer or designer. Rushdi Shams, Lecturer, Dept of CSE, KUET, Bangladesh 4
  • 5. An image that describe fuzzy logic Rushdi Shams, Lecturer, Dept of CSE, KUET, Bangladesh 5
  • 6. An image that describe fuzzy logic  A point on that scale has three "truth values" — one for each of the three functions.  Since the red arrow points to zero, this temperature may be interpreted as "not hot".  The orange arrow (pointing at 0.2) may describe it as "slightly warm" and  the blue arrow (pointing at 0.8) "fairly cold". Rushdi Shams, Lecturer, Dept of CSE, KUET, Bangladesh 6
  • 7. Fuzzy Rules  fuzzy logic usually uses IF-THEN rules  Rules are usually expressed in the form: IF variable IS property THEN action  For example, a simple temperature regulator that uses a fan might look like this: IF temperature IS very cold THEN stop fan IF temperature IS cold THEN turn down fan IF temperature IS normal THEN maintain level IF temperature IS hot THEN speed up fan Rushdi Shams, Lecturer, Dept of CSE, KUET, Bangladesh 7
  • 8. Fuzzy Rules  There is no "ELSE" – all of the rules are evaluated, because the temperature might be "cold" and "normal" at the same time to different degrees.  The AND, OR, and NOT operators of boolean logic exist in fuzzy logic, usually defined as the minimum, maximum, and complement  when they are defined this way, they are called the Zadeh operators Rushdi Shams, Lecturer, Dept of CSE, KUET, Bangladesh 8
  • 9. Zadeh Operators  NOT x = (1 - truth(x))  x AND y = minimum(truth(x), truth(y))  x OR y = maximum(truth(x), truth(y)) Rushdi Shams, Lecturer, Dept of CSE, KUET, Bangladesh 9
  • 10. Hedges  There are also other operators, more linguistic in nature, called hedges that can be applied.  These are generally adverbs such as "very", or "somewhat" Rushdi Shams, Lecturer, Dept of CSE, KUET, Bangladesh 10
  • 11. Fuzzy Logic Applications  Air conditioning  Washing Machines (LG is the pioneer)  Mono-rails (first used in Tokyo)  Digital image processing (specially in medical imaging)  Elevators (in case of power failure)  Rice cookers  Video game engines (disperse intelligence in prince of Persia)  Special effects (swarm intelligence in Batman Begins, Terminator Salvation, The Lord of the Rings) Rushdi Shams, Lecturer, Dept of CSE, KUET, Bangladesh 11
  • 12. Objections against Fuzzy Logic  The concept of "coldness" cannot be expressed in an equation, because although temperature is a quantity, "coldness" is not  people have an idea of what "cold" is, and agree that there is no sharp cutoff between "cold" and "not cold"  where something is "cold" at N degrees but "not cold" at N+1 degrees — a concept classical logic cannot easily handle Rushdi Shams, Lecturer, Dept of CSE, KUET, Bangladesh 12
  • 13. Objections against Fuzzy Logic  The result has no set answer so it is believed to be a 'fuzzy' answer.  Fuzzy logic simply provides a mathematical model of the vagueness which is manifested in the above example. Rushdi Shams, Lecturer, Dept of CSE, KUET, Bangladesh 13
  • 14. A new way to represent probabilistic logic?  fuzzy set theory uses the concept of fuzzy set membership (i.e., how much a variable is in a set)  probability theory uses the concept of subjective probability (i.e., how probable do I think that a variable is in a set). Rushdi Shams, Lecturer, Dept of CSE, KUET, Bangladesh 14
  • 15. Reference  Wikipedia, “Fuzzy Logic”, http://en.wikipedia.org/wiki/Fuzzy_logic Rushdi Shams, Lecturer, Dept of CSE, KUET, Bangladesh 15