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Lógica Fuzzy, Notas de estudo de Engenharia de Produção

Algoritmo Logica Fuzzy

Tipologia: Notas de estudo

Antes de 2010

Compartilhado em 18/12/2009

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Vojislav KECMAN, Department of Mechanical Engineering, The University of Auckland, NZ
Slides accompanying The MIT Press‘ book:: Learning and Soft Computing
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Vojislav KECMAN, Department of Mechanical Engineering, The University of Auckland, NZ

Slides accompanying The MIT Press‘ book:: Learning and Soft Computing

T

he slides shown here are developed around the basic notion of

Embedding Structured Human Knowledge into workable mathematical models

by Fuzzy Logic

as presented in the book

LEARNING AND SOFT COMPUTING

Support Vector Machines, Neural Networks and Fuzzy Logic Models

Author: Vojislav KECMAN

The MIT Press, Cambridge, MA, 2001

ISBN 0-262-11255-

608 pp., 268 illustrations, 47 examples, 155 problems

They are intended to support both the instructors in the development and delivery

of course content and the learners in acquiring the ideas and techniques

presented in the book in a more pleasant way than just reading.

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  • FL attempts to model the way of

reasoning that goes in the human brain.

  • Almost all of human experience is stored

in the form of the IF - THEN rules.

  • Human reasoning is pervasively

approximate, non-quantitative, linguistic,and dispositional (meaning, usuallyqualified).

The World is Not Binary!

Gradual Transitions & Ambiguities at the

Boundaries

Good, Day, Young, Healthy,

YES,

True, Happy, Tall, 0

Bad, Night, Old, Ill

NO

False, Sad, Short, 1

Criteria: When and Why to Apply FL

Criteria: When and Why to Apply FL

  • Human (Structured)

Knowledge Available

  • Mathematical Model

Unknown orImpossible to Obtain

Criteria: When and Why to Apply FL

  • Human (Structured)

Knowledge Available

  • Mathematical Model

Unknown orImpossible to Obtain

  • Process Substantially

Nonlinear

  • Lack of Precise

Sensor Informations

How to Transfer Human

Knowledge Into the Model?

  • Knowledge must be structured !• Possible shortcomings:
    • Knowledge is very subjective category– ‘Experts’ bounce between some extreme poles:
      • Have problems with structuring the knowledge, or• Too aware in his/hers expertise, or• Tend to hide ‘knowledge’, or ...

Solution: Find a Good Expert! There are alwayssome around.

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membership degree, possibility distribution, grade of belonging

Fuzzy Sets Fuzzy Sets

Crisp Sets Crisp Sets

Modeling or Approximating A Function: Modeling or Approximating A Function:

Curve or Surface Fitting

Curve or Surface Fitting

More Different Names in Different Disciplines:

Regression (L or NL), Estimation, Identification, Filtering

Curve Fitting by Using Fuzzy Rules (Patches)

When There Are More Inputs We Try to Approximate

A Surface (2 Inputs) or Hyper-Surface (3 or More Inputs)

Modeling A Function Modeling A Function

Small Number of Rules - Big Patches or Rough Approximation

Fuzzy Patches

Modeling A Function Modeling A Function

More Rules - More Smaller Patches and Better Approximation

Origin of the patches and how do they work?