Intrlligent Systems

CSG2341 Intelligent Systems

Fuzzy Basics and Fuzzy Inference

Approximate knowledge
 It’s quite warm today  When the road is slippery, drive slower

Probabilistic knowledge
 I am going to toss a coin. (Toss) Who thinks it

is a head?
 Probability(head) = ?  Let’s check.  Now who thinks it is a head?

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Probabilistic knowledge
 Who thinks it will rain tomorrow?  What is the forecast?  What will you answer next week?

“Crisp” knowledge
 This coin contains nickel  Nickel is a metal  Metals conduct electricity

Fuzzy/probability/crisp
 Give me three examples of each…

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Fuzzy Logic & Fuzzy Sets

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(a) Boolean Logic.

(b) Multi-valued Logic.

 (x) X Fuzzy S ubset A 1

0 Crisp S ubset A Fuzziness Fuzziness

x

Fuzzy sets example
 See FuzzySets.xls  A small FuzzySet program - FuzzyExample

A Fuzzy assertion
 It is quite cold today  Fuzzy variable – temperature  Fuzzy set – cold temperatures  More examples about height, sportiness,

build??
 Add to FuzzyExample

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Fuzzy operations
 Complement  Containment  Intersection  Union  Draw diagrams

A Fuzzy rule
 Very tall, sporty people are slim.  Add this to the FuzzyExample program

Fuzzy inference (Text p106)
          

Rule: 1 IF project_funding is adequate OR project_staffing is small THEN risk is low Rule: 2 IF project_funding is marginal AND project_staffing is large THEN risk is normal Rule: 3 IF project_funding is inadequate THEN risk is high

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Fuzzy inference steps
1. 2. 3. 4.

Fuzzification Rule evaluation Aggregation Defuzzification

 Mamdani or Sugeno – style  Change example to Sugeno -style

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