Computer Science: An Overview (13th Edition) (What's New in Computer Science)
13th Edition
ISBN: 9780134875460
Author: Glenn Brookshear, Dennis Brylow
Publisher: PEARSON
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Chapter 11, Problem 43CRP
Program Plan Intro
Artificial neural networks:
Artificial neural network is the computer based processing model inspired by biological neuron networks. These types of networks are used in machine learning.
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Consider a fully-connected artificial neural network with one hidden layer, i.e., a multilayer perceptron (MLP), which has 5 inputs, 3 neurons in the hidden layer, and 1 output neuron. The relation between the output y and the inputs x = [x1, . . . , x5] is given by y(x) = f (w, φ(x)), where φ(x) = [φ1(x), φ2(x), φ3(x)]
1. Draw the diagram that shows the inputs, nuerons, connections, correspond-ing weight parameters, and activation functions.
2. Explain the relation y(x) = f (w, φ(x)): write the explicit relation, explainthe role of functions f and φ(x), and state examples of functions.
The neural network given below takes two binary valued inputs x1,x2 ϵ{0, 1} and the activation function is the binary threshold function (h(x)=l if x>0;O otherwise). The OR logical functions does it compute
Select one:
True
False
.. A. A neural network that represent AND logic gate is shown in the figure. Train the neural
network using Backpropagation Algorithm with the following parameters and write the final
weights of the network.
Learning rate = 0.7
Number of epochs = 4
0.2
0.3
1 приt 20
-1 приt <0
X2
Activation Function for output neuron
-0.4
1
Logic ONE =1, Logic ZERO = -1
Chapter 11 Solutions
Computer Science: An Overview (13th Edition) (What's New in Computer Science)
Ch. 11.1 - Prob. 1QECh. 11.1 - Prob. 2QECh. 11.1 - Prob. 3QECh. 11.1 - Prob. 4QECh. 11.1 - Prob. 5QECh. 11.2 - Prob. 1QECh. 11.2 - Prob. 2QECh. 11.2 - Prob. 3QECh. 11.2 - Prob. 4QECh. 11.2 - Identify the ambiguities involved in translating...
Ch. 11.2 - Prob. 6QECh. 11.2 - Prob. 7QECh. 11.3 - Prob. 1QECh. 11.3 - Prob. 2QECh. 11.3 - Prob. 3QECh. 11.3 - Prob. 4QECh. 11.3 - Prob. 5QECh. 11.3 - Prob. 6QECh. 11.3 - Prob. 7QECh. 11.3 - Prob. 8QECh. 11.3 - Prob. 9QECh. 11.4 - Prob. 1QECh. 11.4 - Prob. 2QECh. 11.4 - Prob. 3QECh. 11.4 - Prob. 4QECh. 11.4 - Prob. 5QECh. 11.5 - Prob. 1QECh. 11.5 - Prob. 2QECh. 11.5 - Prob. 3QECh. 11.6 - Prob. 1QECh. 11.6 - Prob. 2QECh. 11.6 - Prob. 3QECh. 11.7 - Prob. 1QECh. 11.7 - Prob. 2QECh. 11.7 - Prob. 3QECh. 11 - Prob. 1CRPCh. 11 - Prob. 2CRPCh. 11 - Identify each of the following responses as being...Ch. 11 - Prob. 4CRPCh. 11 - Prob. 5CRPCh. 11 - Prob. 6CRPCh. 11 - Which of the following activities do you expect to...Ch. 11 - Prob. 8CRPCh. 11 - Prob. 9CRPCh. 11 - Prob. 10CRPCh. 11 - Prob. 11CRPCh. 11 - Prob. 12CRPCh. 11 - Prob. 13CRPCh. 11 - Prob. 14CRPCh. 11 - Prob. 15CRPCh. 11 - Prob. 16CRPCh. 11 - Prob. 17CRPCh. 11 - Prob. 18CRPCh. 11 - Give an example in which the closed-world...Ch. 11 - Prob. 20CRPCh. 11 - Prob. 21CRPCh. 11 - Prob. 22CRPCh. 11 - Prob. 23CRPCh. 11 - Prob. 24CRPCh. 11 - Prob. 25CRPCh. 11 - Prob. 26CRPCh. 11 - Prob. 27CRPCh. 11 - Prob. 28CRPCh. 11 - Prob. 29CRPCh. 11 - Prob. 30CRPCh. 11 - Prob. 31CRPCh. 11 - Prob. 32CRPCh. 11 - Prob. 33CRPCh. 11 - What heuristic do you use when searching for a...Ch. 11 - Prob. 35CRPCh. 11 - Prob. 36CRPCh. 11 - Prob. 37CRPCh. 11 - Prob. 38CRPCh. 11 - Suppose your job is to supervise the loading of...Ch. 11 - Prob. 40CRPCh. 11 - Prob. 41CRPCh. 11 - Prob. 42CRPCh. 11 - Prob. 43CRPCh. 11 - Prob. 44CRPCh. 11 - Prob. 45CRPCh. 11 - Draw a diagram similar to Figure 11.5 representing...Ch. 11 - Prob. 47CRPCh. 11 - Prob. 48CRPCh. 11 - Prob. 49CRPCh. 11 - Prob. 50CRPCh. 11 - Prob. 51CRPCh. 11 - Prob. 52CRPCh. 11 - Prob. 53CRPCh. 11 - Prob. 54CRPCh. 11 - Prob. 1SICh. 11 - Prob. 2SICh. 11 - Prob. 3SICh. 11 - Prob. 4SICh. 11 - Prob. 5SICh. 11 - Prob. 6SICh. 11 - Prob. 7SICh. 11 - Prob. 8SICh. 11 - Prob. 9SICh. 11 - Prob. 10SICh. 11 - Prob. 11SICh. 11 - Prob. 12SICh. 11 - A GPS in an automobile provides a friendly voice...Ch. 11 - Prob. 14SI
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- Let us take an example of a neural network simulating an XNOR function. So if u 0 %3D +1 0.5 -0.5 a, -1 a X, XNOR X2 1.5 X2 You can see that the last neuron takes input from two neurons before it. Suppose X1 is 0 and X2 is 1, what will be the output for the below neural network? 1 0.55 1.5arrow_forwardTwo 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…arrow_forwardFind the output value in the artificial neural network given below.arrow_forward
- 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.arrow_forwardConsider the back propagation neural network as shown in figure 1, assume that the neurons have a logistic sigmoid activation function, do the following: a) Perform a forward pass on the network. b) Perform a reverse pass (training) once (target =1, x=1, n = 0.9) 2 0.004 0.01 0.101 0.35 Z1 0.66 0.205 h 0.703 0.404 0.595 0.55 0855 0.9 0.5 0.072 h 0.246 Z2 0.567 0.445 Figure 1 0.057 0.343 0.007arrow_forwardDraw a neural network (including structure, weights and thresholds) that computes the majority function with 4 inputs, which returns 1 when 2 or more inputs are 1 and returns 0 otherwise. Explain how the network performs the function.arrow_forward
- Design a neural network that has two input nodes x1, x2 and one outpút node y. The to-be-learned function is y= x1 * x2. You can assume that 0 <= x1, x2 <= 1. 2.1( be? How do you obtain your training/validation/test set? How large will each sets Describe your network structure. How many layers : 2, how many nodes in 2.2 each layer and how nodes are connected. 2.3 ( : What is your activation function? 2.4 ( Describe your loss function „How do you update your weights and biases? Show your trained weights/biases 2.5 2.6 (* 2.arrow_forwardHow do I understand that I am getting the correct output when using the sigmoid function in a neural network? Considering that a sigmoid function (activation function) helps with checking the value between 0.00001 to 1.Also, what does it mean if you get output that are around 0.999877 or 0.999955 or within those values? Do these values represent how accurate the neural net is?arrow_forwardThe following Boolean function: f(x1, x2, x3) = 1 if and only if exactly one of x1, x2 and x3 is 1. Construct by hand a neural network that computes f, using the minimal number of units. Make sure to specify what sort of units you are using. Prove that the function f defined in (i) is not implementable with a single perceptron.arrow_forward
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