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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. , (i.e. net = x1*w1+x2*w2+x3*w3), f(net) is the sign of net   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)? Suggest a vector w such that the total error is ZERO for the 10 points.

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. , (i.e. net = x1*w1+x2*w2+x3*w3), f(net) is the sign of net   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)? Suggest a vector w such that the total error is ZERO for the 10 points.

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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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.

, (i.e. net = x1*w1+x2*w2+x3*w3), f(net) is the sign of net

 

  • 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)?
  • Suggest a vector w such that the total error is ZERO for the 10 points.

 

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 = Σx; w₁, (i.e. net = x1*wl+x2*w2+x3*w3), f(net) is the sign of net W = W.1+ c(dt-1-sign (wt.1.xt.1)) xt.1 Use: 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)? Suggest a vector w such that the total error is ZERO for the 10 points. X₁ 1.0 9.4 2.5 8.0 0.5 7.9 7.0 2.8 1.2 7.8 x2 1.0 6.4 2.1 7.7 2.2 8.4 7.0 0.8 3.0 6.1 Output 1 -1 1 -1 1 -1 -1 1 1 -1 10.0 32 5.0- where c is the traing factor c=0.2 0.0- 0.0 P A f(net)=0 5.0 10.0
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 = Σx; w₁, (i.e. net = x1*wl+x2*w2+x3*w3), f(net) is the sign of net W = W.1+ c(dt-1-sign (wt.1.xt.1)) xt.1 Use: 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)? Suggest a vector w such that the total error is ZERO for the 10 points. X₁ 1.0 9.4 2.5 8.0 0.5 7.9 7.0 2.8 1.2 7.8 x2 1.0 6.4 2.1 7.7 2.2 8.4 7.0 0.8 3.0 6.1 Output 1 -1 1 -1 1 -1 -1 1 1 -1 10.0 32 5.0- where c is the traing factor c=0.2 0.0- 0.0 P A f(net)=0 5.0 10.0
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