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(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)
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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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
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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