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Assignment #6 ECE449

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Assignment #6
ECE449, Intelligent Systems Engineering

Points: 10
Due: Thursday, October 24, 2019, 3:30 PM,
in the assignment box in Donadeo ICE
Note: Show your work! Marks are allocated
for technique and not just the answer.
1. [5 points] Consider the following training set

Category:
5/5 - (2 votes)

Assignment #6
ECE449, Intelligent Systems Engineering

Points: 10
Due: Thursday, October 24, 2019, 3:30 PM,
in the assignment box in Donadeo ICE
Note: Show your work! Marks are allocated
for technique and not just the answer.
1. [5 points] Consider the following training set
               
















































4 1
1
1
3 1 4
0
1
2 1 3
1
0
1 0 2
0
0
x 1 = , t =
, x = , t =
, x = , t =
, x = , t =
a) Plot the training samples in the feature space.
b) Apply the perceptron learning rule to the training samples one-at-a-time to obtain weights w1, w2, and
bias w0 that separate the training samples. Use w = [w0, w1, w2] = [0, 0, 0] as initial values (consider bias
input x0 = 1, and learning rate = 1). Write the expression for the resulting decision boundary and draw it
in the graph. [Hint: You can use Excel / OO Calc to implement the learning rule for perceptron, such as
the spreadsheet of InClass_09 posted on eClass].
Epoch Inputs Desired
output t
Initial weights Actual
output y
Error Updated weights
x1 x2 w0 w1 w2 w0 w1 w2
1 0 0 0 0 0 0
0 1 1
1 0 1
1 1 1
2 0 0 0
0 1 1
1 0 1
1 1 1
3 0 0 0
0 1 1
1 0 1
1 1 1
Student Name:
ID Number:
2. [5 points] Consider the following training set
               
















































4 0
1
1
3 1 4
0
1
2 1 3
1
0
1 0 2
0
0
x 1 = , t =
, x = , t =
, x = , t =
, x = , t =
which describes the exclusive OR (XOR) problem.
a) Establish mathematical (not graphical) proof that this problem is not linearly separable. [Hint: Start
with assumption that these patterns are linearly separable, write down equations/inequalities
corresponding to this assumption and examine them for conflict; first such inequality is provided below
as an example.]
Suppose that the problem is linearly separable. The decision boundary can be represented as:
∑ 𝑥𝑖𝑤𝑖 = 0
2
0
or (expanded) 𝑥0𝑤0 + 𝑥1𝑤1 + 𝑥2𝑤2 = 0
This assumption means that either
a) 𝑥0𝑤0 + 𝑥1𝑤1 + 𝑥2𝑤2 < 0𝑓𝑜𝑟 (𝑥1, 𝑥2
) = (0,1) ∧ (𝑥1, 𝑥2
) = (1,0)
𝑥0𝑤0 + 𝑥1𝑤1 + 𝑥2𝑤2 ≥ 0𝑓𝑜𝑟 (𝑥1, 𝑥2
) = (0,0) ∧ (𝑥1, 𝑥2
) = (1,1)
,
or
b) 𝑥0𝑤0 + 𝑥1𝑤1 + 𝑥2𝑤2 > 0𝑓𝑜𝑟 (𝑥1, 𝑥2
) = (0,1) ∧ (𝑥1, 𝑥2
) = (1,0)
𝑥0𝑤0 + 𝑥1𝑤1 + 𝑥2𝑤2 ≤ 0𝑓𝑜𝑟 (𝑥1, 𝑥2
) = (0,0) ∧ (𝑥1, 𝑥2
) = (1,1)
.
must be satisfied. Following one of the cases and putting the values (𝑥1, 𝑥2
) under variables, one obtains
(1) 𝑥0𝑤0 + 𝑤2 < 0
(2)
(3)
(4)

b) Apply the perceptron learning rule following the same procedure as in Problem 1. Describe your
observation.
Epoch Inputs Desired
output t
Initial weights Actual
output y
Error Updated weights
x1 x2 w0 w1 w2 w0 w1 w2
1 0 0 0 0 0 0
0 1 1
1 0 1
1 1 0
2 0 0 0
0 1 1
1 0 1
1 1 0
3 0 0 0
0 1 1
1 0 1
1 1 0

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