Introduction to Softcomputing

1
Introduction to Softcomputing

• Son Kuswadi
• Robotic and Automation Based on
  Biologically-inspired Technology (RABBIT)
• Electronic Engineering Polytechnic Institute of
  Surabaya
• Institut Teknologi Sepuluh Nopember

2
Agenda

• AI and Softcomputing
• From Conventional AI to Computational
  Intelligence
• Neural Networks
• Fuzzy Set Theory
• Evolutionary Computation

3
AI and Softcomputing

• AI predicate logic and symbol manipulation
  techniques

User
Global Database
Inference Engine
Question
User Interface
Explanation Facility
KB

• Fact
• rules

Response
Knowledge Acquisition
Knowledge Engineer
Human Expert
Expert Systems
4
AI and Softcomputing
ANN Learning and adaptation
Fuzzy Set Theory Knowledge representation Via Fuzz
y if-then RULE
Genetic Algorithms Systematic Random Search
5
AI and Softcomputing
ANN Learning and adaptation
Fuzzy Set Theory Knowledge representation Via Fuzz
y if-then RULE
Genetic Algorithms Systematic Random Search
AI Symbolic Manipulation
6
AI and Softcomputing
cat
Animal?
cat
cut
Neural character recognition
knowledge
7
From Conventional AI to Computational Intelligence

• Conventional AI
• Focuses on attempt to mimic human intelligent
  behavior by expressing it in language forms or
  symbolic rules
• Manipulates symbols on the assumption that such
  behavior can be stored in symbolically structured
  knowledge bases (physical symbol system
  hypothesis)

8
From Conventional AI to Computational Intelligence

• Intelligent Systems

Machine Learning
Sensing Devices (Vision)
Perceptions
Task Generator
Inferencing (Reasoning)
Natural Language Processor
Planning
Knowledge Handler
Knowledge Base
Mechanical Devices
Actions
Data Handler
9
Neural Networks
10
Neural Networks
yp(k1)
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e(k1)
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yp(k1)
N
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z-1
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z-1
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Parameter Identification - Parallel
11
Neural Networks
yp(k1)
f
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z-1
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u(k)
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e(k1)
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yp(k1)
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z-1
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Parameter Identification Series Parallel
12
Neural Networks

• Control

Learning Error
Feedforward controller
ANN
-
ANN

Plant



Gp(s)
C(s)
Gc(s)
R(s)
-
Feedback controller
13
Neural Networks

• Control

Current-driven magnetic field
Controller
Iron ball
Ball-position sensor
14
Neural Networks
15
Neural Networks

• Experimental Results

Feedback with ANN Feedforward controller
Feedback control only
Feedback with fixed gain feedforward control
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Fuzzy Sets Theory

• What is fuzzy thinking
• Experts rely on common sense when they solve the
  problems
• How can we represent expert knowledge that uses
  vague and ambiguous terms in a computer
• Fuzzy logic is not logic that is fuzzy but logic
  that is used to describe the fuzziness. Fuzzy
  logic is the theory of fuzzy sets, set that
  calibrate the vagueness.
• Fuzzy logic is based on the idea that all things
  admit of degrees. Temperature, height, speed,
  distance, beauty all come on a sliding scale.
• Jim is tall guy
• It is really very hot today

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Fuzzy Set Theory

• Communication of fuzzy idea

This box is too heavy..
Therefore, we need a lighter one
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Fuzzy Sets Theory

• Boolean logic
• Uses sharp distinctions. It forces us to draw a
  line between a members of class and non members.
• Fuzzy logic
• Reflects how people think. It attempt to model
  our senses of words, our decision making and our
  common sense -gt more human and intelligent systems

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Fuzzy Sets Theory

• Prof. Lotfi Zadeh

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Fuzzy Sets Theory

• Classical Set vs Fuzzy set

No Name Height (cm) Degree of Membership of tall men Degree of Membership of tall men
No Name Height (cm) Crisp Fuzzy
1 Boy 206 1 1
2 Martin 190 1 1
3 Dewanto 175 0 0.8
4 Joko 160 0 0.7
5 Kom 155 0 0.4
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Fuzzy Sets Theory

• Classical Set vs Fuzzy set

Membership value
Membership value
1
1
0
0
175
Height(cm)
175
Height(cm)
Universe of discourse
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Fuzzy Sets Theory

• Classical Set vs Fuzzy set

Let X be the universe of discourse and its
elements be denoted as x. In the classical set
theory, crisp set A of X is defined as function
fA(x) called the the characteristic function of A
In the fuzzy theory, fuzzy set A of universe of
discourse X is defined by function called
the membership function of set A
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Fuzzy Sets Theory

• Membership function

24
Fuzzy Sets Theory

• Fuzzy Expert Systems

Kecepatan (KM)
Jarak (JM)
Posisi Pedal Rem (PPR)
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Fuzzy Sets Theory

• Membership function

PPR
JM
KM
26
Fuzzy Sets Theory

• Fuzzy Rules

Aturan 1 Bila kecepatan mobil cepat sekali dan
jaraknya sangat dekat maka pedal rem diinjak
penuh Aturan 2 Bila kecepatan mobil cukup dan
jaraknya agak dekat maka pedal rem diinjak
sedang Aturan 3 Bila kecepatan mobil cukup dan
jaraknya sangat dekat maka pedal rem diinjak agak
penuh
27
Fuzzy Sets Theory

• Fuzzy Expert Systems

Aturan 1
Cepat Sekali
Sangat Dekat
0 20 40 60 80
0 1 2 3 4
Kecepatan (km/jam)
Jarak (m)
28
Fuzzy Sets Theory

• Fuzzy Expert Systems

Aturan 2
Cukup
0 20 40 60 80
Kecepatan (km/jam)
29
Fuzzy Sets Theory

• Fuzzy Expert Systems

Aturan 3
Cukup
0 20 40 60 80
Kecepatan (km/jam)
30
Fuzzy Sets Theory

• Fuzzy Expert Systems

MOM PPR 200 10x0,220x0,4 COA
PPR 0,20,4 16,670
MOM
COA
0 10 20 30
40
Posisi pedal rem (0)