0.1 0.35 ZI 0.7 0.4 0.85 0.2 h 0.55 0.9 0.5 h Z2 0.4 0.2 0.34. Figure 1 Q/5 A- By using the perceptron rule learning, find the weights required to perform the following classification: vectors [1 -1 -1 1], [-1 -1 1 1] and [1 1 -1 1] are members of class with target value (-1); vector[ 1 1 1 -1] not a member of class with target value (1). Initially the weights and (e) are taken as zero 1 if yin > e Y= F(yin)= 0 if -es yin se -1 if yin <-e B- By using Genetic algorithm, find the value of (y1, y2, y3, y4, y5) to the following equation. F(x) = (7*X1 + 2.4*X2 + 5.5 *X3 + 0.8 *X4 + 6.4*X5) F(x)= 14 ८. //र. Error =1\/f(x) new - f(x) / 3
0.1 0.35 ZI 0.7 0.4 0.85 0.2 h 0.55 0.9 0.5 h Z2 0.4 0.2 0.34. Figure 1 Q/5 A- By using the perceptron rule learning, find the weights required to perform the following classification: vectors [1 -1 -1 1], [-1 -1 1 1] and [1 1 -1 1] are members of class with target value (-1); vector[ 1 1 1 -1] not a member of class with target value (1). Initially the weights and (e) are taken as zero 1 if yin > e Y= F(yin)= 0 if -es yin se -1 if yin <-e B- By using Genetic algorithm, find the value of (y1, y2, y3, y4, y5) to the following equation. F(x) = (7*X1 + 2.4*X2 + 5.5 *X3 + 0.8 *X4 + 6.4*X5) F(x)= 14 ८. //र. Error =1\/f(x) new - f(x) / 3
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...
Related questions
Question
![0.1
0.35
ZI
0.7
0.4
0.85
0.2
h
0.55
0.9
0.5
h
Z2
0.4
0.2
0.34.
Figure 1
Q/5 A- By using the perceptron rule learning, find the weights required to perform the
following classification: vectors [1 -1 -1 1], [-1 -1 1 1] and [1 1 -1 1] are members of
class with target value (-1); vector[ 1 1 1 -1] not a member of class with target value
(1).
Initially the weights and (e) are taken as zero
1 if yin > e
Y= F(yin)=
0
if -es yin se
-1
if yin <-e
B- By using Genetic algorithm, find the value of (y1, y2, y3, y4, y5) to the following
equation.
F(x) = (7*X1 + 2.4*X2 + 5.5 *X3 + 0.8 *X4 + 6.4*X5)
F(x)= 14
८. //र.
Error =1\/f(x) new - f(x) /
3](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F7c3229f8-b431-499b-9dc6-ee1e450f0aa7%2F5aeef027-4cda-42bf-8caf-2870cb75ff37%2Fhomqlbg_processed.jpeg&w=3840&q=75)
Transcribed Image Text:0.1
0.35
ZI
0.7
0.4
0.85
0.2
h
0.55
0.9
0.5
h
Z2
0.4
0.2
0.34.
Figure 1
Q/5 A- By using the perceptron rule learning, find the weights required to perform the
following classification: vectors [1 -1 -1 1], [-1 -1 1 1] and [1 1 -1 1] are members of
class with target value (-1); vector[ 1 1 1 -1] not a member of class with target value
(1).
Initially the weights and (e) are taken as zero
1 if yin > e
Y= F(yin)=
0
if -es yin se
-1
if yin <-e
B- By using Genetic algorithm, find the value of (y1, y2, y3, y4, y5) to the following
equation.
F(x) = (7*X1 + 2.4*X2 + 5.5 *X3 + 0.8 *X4 + 6.4*X5)
F(x)= 14
८. //र.
Error =1\/f(x) new - f(x) /
3
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