Question: An artificial neural network consists of only two layers. Three neurons are in the input layer and(0.1'one in the output layer. The initial weights are w =\0.1/0.1Use the following algorithm to determine the new weights after one iteration for an input x =19.06.0 ) if the desired output is 0.\1.0,1Use f(net) =Ex.wnet =1+e-netLet the learning factor } = 1.0Solution:1- Calculate the actual outputactual_output =f(net) =2- Calculate the differentiation of f (net) at the output neuronf (net) =f(net) (1-f(net)) =3- Calculate the error term at the output neuron8 = (desired – actual_output) f (net) =4- Update the weights (there are 3 weights to be updated) AW=5 * input * 8Wnew = Wold + AW

Given, Weights, w1=0.1, w2=0.1, w3=0.1 Inputs, x1=9.0, x2=6.0, x3=1.0 Desired output = 0 Learning…

An artificial neural network consists of only two layers. Three neurons are in the input layer and (0.1' one in the output layer. The initial weights are w =( 0.1 \0.1. Use the following algorithm to determine the new weights after one iteration for an input x = (9.0) 6.0 if the desired output is 0. 1 Use f(net)= 1+ net = Ex,w. %3D %3D - net e Let the learning factor } = 1.0 Solution: 1- Calculate the actual output actual _output =f(net) = %3D 2- Calculate the differentiation of f (net) at the output neuron f(net) =f(net) (1-f(net)) = 3- Calculate the error term at the output neuron 8 = (desired – actual _output)'f (net) = 4- Update the weights (there are 3 weights to be updated) AW=G * input * 8 Wnew = Wold+AW

An artificial neural network consists of only two layers. Three neurons are in the input layer and (0.1' one in the output layer. The initial weights are w =( 0.1 \0.1. Use the following algorithm to determine the new weights after one iteration for an input x = (9.0) 6.0 if the desired output is 0. 1 Use f(net)= 1+ net = Ex,w. %3D %3D - net e Let the learning factor } = 1.0 Solution: 1- Calculate the actual output actual _output =f(net) = %3D 2- Calculate the differentiation of f (net) at the output neuron f(net) =f(net) (1-f(net)) = 3- Calculate the error term at the output neuron 8 = (desired – actual _output)'f (net) = 4- Update the weights (there are 3 weights to be updated) AW=G * input * 8 Wnew = Wold+AW

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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Question

Question:

An artificial neural network consists of only two layers. Three neurons are in the input layer and
(0.1'
one in the output layer. The initial weights are w =
\0.1/
0.1
Use the following algorithm to determine the new weights after one iteration for an input x =
19.0
6.0 ) if the desired output is 0.
\1.0,
1
Use f(net) =
Ex.w
net =
1+e-net
Let the learning factor } = 1.0
Solution:
1- Calculate the actual output
actual_output =f(net) =
2- Calculate the differentiation of f (net) at the output neuron
f (net) =f(net) (1-f(net)) =
3- Calculate the error term at the output neuron
8 = (desired – actual_output) f (net) =
4- Update the weights (there are 3 weights to be updated) AW=5 * input * 8
Wnew = Wold + AW

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