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Fuzzy Logic Control (EELE 5480) Chapter 1: Introduction Ahmad H. El Khateb
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Fuzzy Logic Control (EELE 5480) Chapter 1: Introduction Ahmad H. El Khateb.

Dec 20, 2015

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Page 1: Fuzzy Logic Control (EELE 5480) Chapter 1: Introduction Ahmad H. El Khateb.

Fuzzy Logic Control (EELE 5480)Chapter 1: Introduction

Ahmad H. El Khateb

Page 2: Fuzzy Logic Control (EELE 5480) Chapter 1: Introduction Ahmad H. El Khateb.

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CONTENTS

Uncertainty and Information

The Case of Imprecision

Fuzzy Sets and Membership

Chance versus Fuzziness

Conclusion and Homework

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The Case of Imprecision

Our understanding of most physical processes is based largely on imprecise human reasoning.

This imprecision (when compared to the precise quantities required by computers) is a form of information that can be quite useful to humans

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The Case of Imprecision

Precision Examples• Shooting precision laser beams over tens of kilometers in space• Milling machine components to accuracies of parts per billion• Focusing a microscopic electron beam on a point of a nanometer.

Imprecision Examples• Parking a car, • Navigating a car among others on a freeway• Washing clothes• Controlling traffic at intersections• A preliminary understanding of a complex system.

Undoubtedly this Imprecision cannot solve problems that require precision

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Uncertainty and Information

Only a small part of the knowledge (information) for a typical problem might be regarded as certain, or deterministic.

Unfortunately, the vast majority of the material taught in engineering classes is based on the presumption that the knowledge involved is deterministic.

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Uncertainty and Information

It is only after we leave the university, it seems, that we realize we were duped in academe, and that the information we have for a particular problem virtually always contains uncertainty. For how many of our problems can we say that the information content is known absolutely? Uncertain information can take on many different forms.

When students graduate, it seems that their biggest fear upon entering the real world is ‘‘forgetting the correct formula.’’ These formulas typically describe a deterministic process.

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Fuzzy Sets and Membership

Crisp Set Fuzzy Set

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Fuzzy Sets and Membership

“Strong Fever”

40.1°C40.1°C

42°C42°C

41.4°C41.4°C

39.3°C39.3°C

38.7°C38.7°C

37.2°C37.2°C

38°C38°C

40.1°C40.1°C

42°C42°C

41.4°C41.4°C

39.3°C39.3°C

38.7°C

37.2°C

38°C

“Strong Fever”

TallTall ShortShort

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Chance versus Fuzziness

Fuzziness describes the lack of distinction of an event, whereas chance describes the uncertainty in the occurrence of the event.

Suppose you are seated at a table on which rest two glasses of liquid. The liquid in the first glass is described to you as having a 95% chance of being healthful and good. The liquid in the second glass is described as having a 0.95 membership in the class of ‘‘healthful and good’’ liquids. Which glass would you select, keeping in mind that the first glass has a 5% chance of being filled with nonhealthful liquids, including poisons.

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Chance versus Fuzziness

• The geometric shape below is an ellipse, with parameters a and b. • Under what conditions of these two parameters will a general elliptic shape become a circle? Mathematically, we know that a circle results when a/b = 1, and hence this is a specific, crisp geometric shape. We know that when a/b>1 or a/b<1 we clearly have an elliptic shape; and as a/b→∞, a line segment results. • Using this knowledge, we can develop a description of the membership function to describe the geometric set we call an ‘‘approximate circle.’’

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Homework

Problems:1.11.31.41.51.111.14

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Homework

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Homework

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Homework

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Homework

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Homework

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Homework

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The EndThe End