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Algoritmo Logica Fuzzy
Tipologia: Notas de estudo
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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
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.
±5diih ;^VXR \Ph QT eXTfTS Pb P±5diih ;^VXR \Ph QT eXTfTS Pb P±5diih ;^VXR \Ph QT eXTfTS Pb P±5diih ;^VXR \Ph QT eXTfTS Pb PQaXSVT ^eTa cWT TgRTbbXeT[h fXSTQaXSVT ^eTa cWT TgRTbbXeT[h fXSTQaXSVT ^eTa cWT TgRTbbXeT[h fXSTQaXSVT ^eTa cWT TgRTbbXeT[h fXSTVP_ QTcfTT] cWT aTRXbX^] ^UVP QTcfTT] cWT aTRXbX^] ^UVP QTcfTT] cWT aTRXbX^] ^UVP QTcfTT] cWT aTRXbX^] ^UR[PbbXRP[ RaXb [^VXR P]S cWTR[PbbXRP[ RaXb_ [^VXR P]S cWTR[PbbXRP[ RaXb_ [^VXR P]S cWTR[PbbXRP[ RaXb_ [^VXR P]S cWTX_aTRXbX^] ^U Q^cW cWT aTP[ f^a[SX_aTRXbX^] ^U Q^cW cWT aTP[ f^a[SX_aTRXbX^] ^U Q^cW cWT aTP[ f^a[SX_aTRXbX^] ^U Q^cW cWT aTP[ f^a[SP]S Xcb Wd\P] X]cTa_aTcPcX^]≤P]S Xcb Wd\P] X]cTa_aTcPcX^]≤P]S Xcb Wd\P] X]cTa_aTcPcX^]≤P]S Xcb Wd\P] X]cTa_aTcPcX^]≤
?PaP_WaPbX]V ;„ IPSTW?PaP_WaPbX]V ;„ IPSTW?PaP_WaPbX]V ;„ IPSTW?PaP_WaPbX]V ;„ IPSTW
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
Knowledge Available
Unknown orImpossible to Obtain
Knowledge Available
Unknown orImpossible to Obtain
Nonlinear
Sensor Informations
How to Transfer Human
Knowledge Into the Model?
Solution: Find a Good Expert! There are alwayssome around.
1
1
0
0
μμμμ
μ μ
μ μ
μμμμ
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?