1 Pertemuan 26 NEURO FUZZY SYSTEM Matakuliah: H0434/Jaringan Syaraf Tiruan Tahun: 2005 Versi: 1.

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1 1 Pertemuan 26 NEURO FUZZY SYSTEM Matakuliah: H0434/Jaringan Syaraf Tiruan Tahun: 2005 Versi: 1

2 2 Learning Outcomes Pada akhir pertemuan ini, diharapkan mahasiswa akan mampu : Menguraikan kaitan antara jaringan syaraf tiruan dengan logika fuzzy.

3 3 Outline Materi Model Neuro-Fuzzy System. Aplikasi Neuro-Fuzzy System.

4 4 SOFT COMPUTING GA FL ANN Learning Capability Every combi is possible and used: Goal is to realize processing systems with greater intelligence Optimizing Capability Representing Capability

5 5 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

6 6 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

7 7 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.

8 8 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.

9 9 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.

10 10 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)

11 11 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 command outputs or decisions.

12 12 MODEL 2 Neural networks are used to tune membership functions of fuzzy systems that are employed as decision-making systems for controlling equipment.

13 13 ANFIS ARCHITECTURE Adaptive Network Fuzzy Inference System

14 14 SUGENO REASONING

15 15 LAYER 1  Output dari neuron layer 1 adalah derajat keanggotaan dari fungsi keanggotaan bell shape.  fungsi keanggotaan bentuknya bisa segitiga dll.

16 16 LAYER 2 Operasi menggunakan MIN dari A dan B

17 17 INFERENCE PROCESS

18 18 LAYER 3 Output layer 3 merupakan normalisasi dari input pada layer 3.

19 19 LAYER 4 Output layer 4 merupakan perkalian dari  dan hasil inference pada layer 2.

20 20 LAYER 5 CRISP OUTPUT Zo :

21 21 ERROR FUNCTION Jika Crisp Training Set diberikan : { ( x k, y k ), k = 1, 2, …….K } Maka error function untuk pattern k : E k = ( y k – o k ) 2 Dimana : y k = output yang diinginkan o k = output dari jaringan

22 22 LEARNING ALGORITHM  Menggunakan steepest descent method

23 23 PHOTOCOPIER MACHINE ( MATSUSHITA ELECTRIC )

24 24 WASHING MACHINE ( HITACHI )