Introduction to Softcomputing
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Transcript of Introduction to Softcomputing
Introduction to Softcomputing
Son KuswadiRobotic and Automation Based on Biologically-inspired Technology (RABBIT)Electronic Engineering Polytechnic Institute of SurabayaInstitut Teknologi Sepuluh Nopember
Agenda
AI and Softcomputing From Conventional AI to
Computational Intelligence Neural Networks Fuzzy Set Theory Evolutionary Computation
AI and Softcomputing
AI: predicate logic and symbol manipulation techniques
Use
r In
terf
ace
Inference Engine
Explanation Facility
Knowledge Acquisition
KB: •Fact•rules
GlobalDatabase
KnowledgeEngineer
HumanExpert
Question
Response
Expert Systems
User
AI and Softcomputing
ANNLearning and
adaptation
Fuzzy Set TheoryKnowledge representation
ViaFuzzy if-then RULE
Genetic AlgorithmsSystematic
Random Search
AI and Softcomputing
ANNLearning and
adaptation
Fuzzy Set TheoryKnowledge representation
ViaFuzzy if-then RULE
Genetic AlgorithmsSystematic
Random Search
AISymbolic
Manipulation
AI and Softcomputing
cat
cut
knowledge
Animal? cat
Neural characterrecognition
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)
From Conventional AI to Computational Intelligence
Intelligent Systems
Sensing Devices(Vision)
Natural Language Processor
MechanicalDevices
Perceptions
Actions
TaskGenerator
KnowledgeHandler
DataHandler Knowledge
Base
MachineLearning
Inferencing(Reasoning)
Planning
Neural Networks
Neural Networks
f
z-1
z-1
0
1
N
z-1
z-1
1
+
-
e(k+1)
0
^
^
yp(k+1)
yp(k+1)^
u(k)
Parameter Identification - Parallel
Neural Networks
ff
z-1
z-1
0
1
NN
z-1
z-1
1
+
-
e(k+1)
0
^
^
yp(k+1)
yp(k+1)^
u(k)
Parameter Identification – Series Parallel
Neural Networks
Control
ANN
Gp(s)Gc(s)R(s) C(s)+
-
+
+
Plant
Feedforward controller
Feedback controller
ANN-
+
Learning Error
Neural Networks
Control
Ball-position sensor
ControllerCurrent-driven magnetic field
Iron ball
Neural Networks
Neural Networks
Experimental Results
Feedback controlonly
Feedback with fixed gain feedforward control
Feedback with ANNFeedforward controller
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
Fuzzy Set Theory
Communication of “fuzzy “ idea
This box is too heavy.. Therefore, we
need a lighter one…
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 -> more human and intelligent systems
Fuzzy Sets Theory
Prof. Lotfi Zadeh
Fuzzy Sets Theory
Classical Set vs Fuzzy set
No NameHeight
(cm)
Degree of Membership of “tall men”
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
Fuzzy Sets Theory
Classical Set vs Fuzzy set
1
0175 Height(cm)
1
0175 Height(cm)
Universe of discourse
Membership value Membership value
Fuzzy Sets Theory
Classical Set vs Fuzzy set
Ax
AxxfXxf AA if,0
if,1)(where},1,0{:)(
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)(xA
.inpartlyisif1)(0
;innotisif0)(
;intotallyisif1)(],1,0[:)(
Axx
Axx
AxxwhereXx
A
A
AA
Fuzzy Sets Theory
Membership function
0 2 4 6 8 10
0.2
0.4
0.6
0.8
1
Derajat keanggotaan [0, 1]
elemen semesta pembicaraan
A
0
1
0.5
c-b c-b/2 c c+b/2 c+b
b
Fuzzy Sets Theory
Fuzzy Expert Systems
Kecepatan (KM)
Jarak (JM)
Posisi Pedal Rem (PPR)
Fuzzy Sets Theory
Membership function
Kecepatan (km/jam)
0 20 40 60 80
Sangat Lambat
Lambat
Cukup
Cepat
Cepat Sekali
Jarak (m)
0 1 2 3 4
Sangat Dekat
AgakDekat
Sedang
Agak Jauh
Jauh Sekali
Posisi pedal rem (0)
0 10 20 30 40
Injak Penuh
Injak Agak Penuh
InjakSedang
Injak Sedikit
Injak Sedikit Sekali
KM JM PPR
Fuzzy Sets Theory
Fuzzy Rules
Aturan 1: Bila kecepatan mobil cepat sekali dan jaraknya sangat dekat maka pedal rem diinjak penuhAturan 2:Bila kecepatan mobil cukup dan jaraknya agak dekat maka pedal rem diinjak sedangAturan 3:Bila kecepatan mobil cukup dan jaraknya sangat dekat maka pedal rem diinjak agak penuh
Fuzzy Sets Theory
Fuzzy Expert Systems
Aturan 1:
Kecepatan (km/jam)
0 20 40 60 80
Cepat Sekali
Posisi pedal rem (0)
0 10 20 30 40
Injak Penuh
Jarak (m)
0 1 2 3 4
Sangat Dekat
Fuzzy Sets Theory
Fuzzy Expert Systems
Jarak (m)
0 1 2 3 4
Agak Dekat
Posisi pedal rem (0)
0 10 20 30 40
Injak Sedang
Aturan 2:
Kecepatan (km/jam)
0 20 40 60 80
Cukup
Fuzzy Sets Theory
Fuzzy Expert Systems
Jarak (m)
0 1 2 3 4
Sangat Dekat
0 10 20 30 40
Posisi pedal rem (0)
Injak Agak Penuh
Kecepatan (km/jam)
0 20 40 60 80
Cukup
Aturan 3:
Fuzzy Sets Theory
Fuzzy Expert Systems
MOM : PPR = 200
10x0,2+20x0,4COA : PPR =
0,2+0,4 = 16,670
Posisi pedal rem (0)
0 10 20 30 40
MOMCOA