1. Pertemuan 7 JARINGAN INSTAR DAN OUTSTAR
Matakuliah : H0434/Jaringan Syaraf Tiruan
Tahun : 2005
Versi : 1
Pertemuan 7 JARINGAN INSTAR DAN OUTSTAR

2. Menghubungkan Instar dan Outstar Rule pada Jaringan Associative
Learning Outcomes
Pada akhir pertemuan ini, diharapkan mahasiswa
akan mampu :
Menghubungkan Instar dan Outstar Rule pada Jaringan Associative

3. Arsitektur Jaringan Instar Arsitektur Jaringan Outstar Learning Rule
Outline Materi
Arsitektur Jaringan Instar
Arsitektur Jaringan Outstar
Learning Rule

4. INSTAR
RECOGNITION NETWORK

5. INSTAR OPERATION The instar will be active when or1 p q
cos b – =
For normalized vectors, the largest inner product occurs when the angle between the weight vector and the input vector is zero -- the input vector is equal to the weight vector.
The rows of a weight matrix represent patterns
to be recognized.

6. INSTAR RULE Hebb with Decay
Modify so that learning and forgetting will only occur
when the neuron is active - Instar Rule: w i j q ( ) 1 – a p g + = or
Vector Form:

7. GRAPHICAL REPRESENTATION
For the case where the instar is active (ai = 1): or
For the case where the instar is inactive (ai = 0):

8. CONTOH

9. TRAINING
First Iteration (a=1):

10. Orange will now be detected if either set of sensors works.
Further Training
(orange) h a 2 ( ) r d l i m w p W – + = 3 1 × è ø ç ÷ æ ö a 3 ( ) h r d l i m w p W 2 – + =
(orange) × 1 è ø ç ÷ æ ö
Orange will now be detected if either set of sensors works.

11. Kohonen Rule Learning occurs when the neuron’s index i is a member of
the set X(q). We will see in Chapter 14 that this can be used
to train all neurons in a given neighborhood.

12. OUTSTAR
RECALL NETWORK

13. Outstar Operation
Suppose we want the outstar to recall a certain pattern a*
whenever the input p = 1 is presented to the network. Let
Then, when p = 1
and the pattern is correctly recalled.
The columns of a weight matrix represent patterns
to be recalled.

14. Outstar Rule
For the instar rule we made the weight decay term of the Hebb
rule proportional to the output of the network. For the outstar
rule we make the weight decay term proportional to the input of the network.
If we make the decay rate g equal to the learning rate a,
Vector Form:

15. Example - Pineapple Recall

16. Definitions

17. Iteration 1
a = 1

18. Convergence