solution: homework 6

8
Dr.-Ing. Erwin Sitompul President University Lecture 9 Introduction to Neural Networks and Fuzzy Logic President University Erwin Sitompul NNFL 9/1 http://zitompul.wordpress.com

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Fuzzy Logic. Fuzzy Control. Solution: Homework 6. v. small. small. perfect. big. v. big. growing. declining. constant. 1. 1. 0. 75. 0.6. 0.4. 0.25. 0 5 10 15 20 25. – 10 – 5 0 5 10. speed change [m/s 2 ]. distance to next car [m]. - PowerPoint PPT Presentation

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Page 1: Solution:  Homework 6

Dr.-Ing. Erwin SitompulPresident University

Lecture 9

Introduction to Neural Networksand Fuzzy Logic

President University Erwin Sitompul NNFL 9/1

http://zitompul.wordpress.com

Page 2: Solution:  Homework 6

President University Erwin Sitompul NNFL 9/2

v. small

distance to next car [m]

small perfect big v. big

0 5 10 15 20 25

1

–small zero+small +big–big

acceleration adj. [m/s2] –2 –1 0 1 2

1

1

speed change [m/s2]

constant growingdeclining

–10 –5 0 5 10

Solution: Homework 6

2.5 m/s2

0.75

0.25

13 m

0.6

0.4

Fuzzy ControlFuzzy Logic

Page 3: Solution:  Homework 6

President University Erwin Sitompul NNFL 9/3

Rule 1: IF distance is small AND speed is declining, THEN maintain acceleration.

Rule 2: IF distance is small AND speed is constant, THEN acceleration adjustment negative

small.

Rule 3: IF distance is perfect AND speed is declining, THEN acceleration adjustment positive small.

Rule 4: IF distance is perfect AND speed is constant, THEN maintain acceleration.

Solution: Homework 6 (Cont.)0.4

0.4

0.6

0.6

0.75

0.75

0

0

FL-Operators:AND MinOR Max

0.4

0

0

0.6

Fuzzy ControlFuzzy Logic

Page 4: Solution:  Homework 6

President University Erwin Sitompul NNFL 9/4

Solution: Homework 6 (Cont.)

0.6

acceleration change [m/s2] –2 –1 0 1 2

1

A1

A22 ( )A h a b

1 0.4 2 (2.2 3) 1.04A

2 0.2 2 (0.8 1.2) 0.2A

1 1 2 2

1 2x

c A c Ac

A A

–small zero+small +big–big

acceleration adj. [m/s2] –2 –1 0 1 2

1

0.4

1 0.5c

2 0c

20.4194 m s

Fuzzy ControlFuzzy Logic

Page 5: Solution:  Homework 6

President University Erwin Sitompul NNFL 9/5

Implementation in Matlab

Rule Viewer

Inp

ut

1 M

Fs

Inp

ut

2 M

Fs

Ou

tpu

t M

Fs

Fuzzy ControlFuzzy Logic

Solution: Homework 6 (Cont.)

Page 6: Solution:  Homework 6

President University Erwin Sitompul NNFL 9/6

Fuzzy Logic Toolbox in MatlabFuzzy ControlFuzzy Logic

The toolbox can be opened by typing “fuzzy” in Matlab Workspace

Some variables must be defined: Number of inputs and outputs Membership functions of each

input and output Fuzzy rules that will connect

the membership functions Fuzzy set operators, inference

core, accumulation, and defuzzification

Page 7: Solution:  Homework 6

President University Erwin Sitompul NNFL 9/7

Fuzzy Logic Toolbox in MatlabFuzzy ControlFuzzy Logic

Now, we utilize the fuzzy toolbox to analyze the input-output behavior of the fuzzy control.

Later, the resulting fuzzy control can be applied to control dynamic systems in Simulink environment.

In each session, remember to save the controller that has been designed: Save files using Files >> Export >> To Disk Open files using Files >> Import >> From Disk

Page 8: Solution:  Homework 6

President University Erwin Sitompul NNFL 9/8

Homework 7Fuzzy ControlFuzzy Logic

Read the manual of Fuzzy Logic Toolbox carefully. Learn how to use the toolbox and get familiar with it. Redo the quiz problem in Matlab. Submit the *.fis file of

the fuzzy control along with necessary supporting documents.

Submission must be in hardcopy and softcopy. Incomplete submission will not be graded.