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Matakuliah
Tahun
Versi
: H0434/Jaringan Syaraf Tiruan
: 2005
:1
Pertemuan 26
NEURO FUZZY SYSTEM
1
Learning Outcomes
Pada akhir pertemuan ini, diharapkan mahasiswa
akan mampu :
• Menguraikan kaitan antara jaringan syaraf
tiruan dengan logika fuzzy.
2
Outline Materi
• Model Neuro-Fuzzy System.
• Aplikasi Neuro-Fuzzy System.
3
SOFT COMPUTING
ANN
Learning
Capability
Optimizing
Capability
GA
Every combi is
possible and used:
FL
Representing
Capability
Goal is to realize
processing
systems with
greater
4
intelligence
SOFT COMPUTING

Real world problems




Ill-defined
Imprecisely formulated
Mimics human brain
Reasoning and decision making exploits :
 imprecision
 uncertainty
 approximate reasoning
 partial truth

to have robust, low-cost solutions
5
COMPONENTS OF SOFT
COMPUTING



Fuzzy Logic
Artificial Neural Network
Genetic Algorithm


The components are complementary
and synergistic
Better results, if used in combination,
rather than in stand-alone
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FUZZY NEURAL NETWORK
• While fuzzy logic provides an inference
mechanism under cognitive uncertainty,
computational neural networks oÆer exciting
advantages, such as learning, adaptation,
fault-tolerance, parallelism and generalization.
• To enable a system to deal with cognitive
uncertainties in a manner more like humans,
one may incorporate the concept of fuzzy logic
into the neural networks.
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NEURAL-FUZZY COMPARISON
• NEURAL NETWORK
– Good in pattern recognition.
– Not good at explaining how to reach the
decision.
 FUZZY LOGIC
 Can reason with inprecise information.
 Good at explaining the decision.
 Can’t automatically acquire the rules.
8
TUJUAN NEURAL-FUZZY
To enable a system to deal with cognitive
uncertainties in a manner more like
humans, one may incorporate the concept
of fuzzy logic into the neural networks.
9
PROSES NEURAL-FUZZY
Mulai dari pengembangan “ Fuzzy Neuron “
diikuti dengan mekanisme pembelajaran (
learning )
• development of fuzzy neural models
motivated by biological neurons,
• models of synaptic connections which
incorporates fuzziness into neural network,
• development of learning algorithms (that is
the method of adjusting the synaptic
weights)
10
MODEL 1
In response to linguistic statements, the fuzzy interface block
provides an input vector to a multi-layer neural network. The
neural network can be adapted (trained) to yield desired
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command outputs or decisions.
MODEL 2
Neural networks are used to tune membership functions
of fuzzy systems that are employed as decision-making
systems for controlling equipment.
12
ANFIS ARCHITECTURE
Adaptive Network Fuzzy Inference System
13
SUGENO REASONING
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LAYER 1
 Output dari neuron layer 1 adalah derajat
keanggotaan dari fungsi keanggotaan bell
shape.
 fungsi
keanggotaan
segitiga dll.
bentuknya
bisa
15
LAYER 2
Operasi menggunakan MIN dari A dan B
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INFERENCE PROCESS
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LAYER 3
Output layer 3 merupakan normalisasi
dari input pada layer 3.
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LAYER 4
Output layer 4 merupakan perkalian dari 
dan hasil inference pada layer 2.
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LAYER 5
CRISP OUTPUT Zo :
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ERROR FUNCTION
Jika Crisp Training Set diberikan :
{ ( xk, yk ), k = 1, 2, …….K }
Maka error function untuk pattern k :
Ek = ( yk – ok )2
Dimana : yk = output yang diinginkan
ok = output dari jaringan
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LEARNING ALGORITHM
Menggunakan steepest descent method
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PHOTOCOPIER MACHINE
( MATSUSHITA ELECTRIC )
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WASHING MACHINE
( HITACHI )
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