ANNs- unsupervised Model-ART1

1. Soft Computing: Artificial Neural
Networks-Unsupervised Model-ART1
Dr. Baljit Singh Khehra
Professor
CSE Department
Baba Banda Singh Bahadur Engineering College
Fatehgarh Sahib-140407, Punjab, India

2. ART1
 Binary Adaptive Resonance Theory
 Designed to perform for binary input vector
 Unsupervised Learning
 ART1 has
 F1 units (Input and Interface units)
 F2 units (Cluster units)
 A Reset unit:
implements user Control over the degree
of similarity of patterns placed on the same
cluster

3. ART1
Step1. Initialize Parameters
Input Units (n) , Cluster Units (m) , L >1
Vigilance parameter ( )
Bottom-up weights (bij)
Top-down weights
Tji (0) =1
Step2. While stopping condition is not satisfied, do Steps 3-14
Step3. For each training input pattern, do Steps 4-13
Here training input pattern is s
Step 4. Set activation of all F2 units is zero
Set activation of all F1(a) units is input vector (s)
10  
nL
L
bij


1
)0(0

4. ART1
Step 5. Compute norm of s
Step.6 Send input signal from F1(a) to F1(b) layer
xi = si
Step.7 Calculate net input
j=1,2,3,………,m
i=1,2,3,……….,n
Find yJ such that yJ ≥ yj for j=1,2,3,………,m
Step 8. While reset is true do Steps 9-12
Step 9. The winning unit is J
Step 10. Recompute activation of F1


n
i
iss
1


n
i
ijij bxy
1
Jiii tsx 

5. ART1
Step 11. Calculate norm of x
Step 12. Test for reset
yJ = -1, proceed to step 8
Go to Step 13
Step13. Update weights
Step14. Test for stopping condition


n
i
ixx
1
Falseisresetthen
s
x
if 
Trueisresetthen
s
x
if 
xL
Lx
newb i
iJ


1
)(
iJi xnewt )(

6. ART1 Example
Step1. Initialize Parameters
Input Units (n) = 9,
Cluster Units (m) = 2, L = 2
Input Pattern is [ 1 1 1 1 0 1 1 1 1]
Step2. While stopping condition is not satisfied,
do Steps 3-14
Step3. For each training input pattern, do Steps 4-13
Here training input pattern is [ 1 1 1 1 0 1 1 1 1]
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jit

7. ART1
Step 4. Set activation of all F2 units is zero
Set activation of all F1(a) units is input vector
s = [ 1 1 1 1 0 1 1 1 1]
Step 5. Compute norm of s
Step.6 Send input signal from F1(a) to F1(b) layer
xi = si
x = [ 1 1 1 1 0 1 1 1 1]
Step.7 Calculate net input
y1 = 2
y2 = 1.6
y1 > y2
J = 1
8
1
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n
i
iss


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i
ijij bxy
1

8. ART1
Step 8. While reset is true do Steps 9-12
Step 9. The winning unit is
J = 1
Step 10. Recompute activation of F1
x = [ 1 1 1 1 0 1 1 1 1] [ 1 0 1 0 1 0 1 0 1]
x = [ 1 0 1 0 0 0 1 0 1]
Step 11. Calculate norm of x
Step 12. Test for reset
Go to Step 13
Else
yJ = -1, proceed to step 8
Jiii tsx 
4
1
 
n
i
ixx

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x
s
x
,4.05.08/4
falseisresetthen
s
x
if 

9. ART1
Step13. Update weights
Step14. Test for stopping condition
5
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iJ
x
xL
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