(please note that sometimes we use the symbol { instead of c) What are the new values of the vector w after the first input (1,1,1), note that input x3 is always 1.0? What are the new values of the vector w after the second input (9.4, 6.4, 1.0)? What is the Error if the decision boundary is x1*2+x2*2-12 =0, (ig W=(2, 2, -12).
(please note that sometimes we use the symbol { instead of c) What are the new values of the vector w after the first input (1,1,1), note that input x3 is always 1.0? What are the new values of the vector w after the second input (9.4, 6.4, 1.0)? What is the Error if the decision boundary is x1*2+x2*2-12 =0, (ig W=(2, 2, -12).
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Transcribed Image Text:Two layers neural networks consist of 3 input neurons and one output neuron. The input vector is
(x1, x2, x3) where x3 is always =1.0.
x1, x2 and the desired output d are given by the table below.
When using the initial weight vector w = (1.0, 1.0, -3.0) there was an error as shown in the
figure. The equation for the decision boundary was: x1*1 + x2*1 - 1*3=0.
net =Exw., (i.e. net = x1*wl+x2*w2+x3*w3), f(net) is the sign of net
w' = w1 + c(d' -1- sign (Wt -1• xt-1)) x' -1
Use:
1.0,
where c is the training (learning) factor and c =
( please note that sometimes we use the symbol } instead of c)
What are the new values of the vector w after the first input (1,1,1), note that input x3 is
always 1.0?
What are the new values of the vector w after the second input (9.4, 6.4, 1.0)?
What is the Error if the decision boundary is x1*2+x2*2-12 =0, (ie W=(2, 2, -12).
10.0
X1
X2
Output
1.0
1.0
1
9.4
6.4
-1
2.5
2.1
8.0
7.7
-1
X2 5.0-
0.5
2.2
7.9
8.4
-1
7.0
7.0
-1
f(net)=0
A
2.8
0.8
1
0.0
0.0
1.2
3.0
1
5.0
X1
10.0
7.8
6.1
-1
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