EBK COMPUTER SCIENCE: AN OVERVIEW
12th Edition
ISBN: 8220102744196
Author: BRYLOW
Publisher: PEARSON
expand_more
expand_more
format_list_bulleted
Concept explainers
Question
Chapter 11, Problem 46CRP
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.
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solution
Students have asked these similar questions
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
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.
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
1.5
Chapter 11 Solutions
EBK COMPUTER SCIENCE: AN OVERVIEW
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. 11.5 - Prob. 4QECh. 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 - Prob. 46CRPCh. 11 - Prob. 47CRPCh. 11 - Prob. 48CRPCh. 11 - Draw a diagram similar to Figure 11.5 representing...Ch. 11 - Prob. 50CRPCh. 11 - Prob. 51CRPCh. 11 - Prob. 52CRPCh. 11 - Prob. 53CRPCh. 11 - Prob. 54CRPCh. 11 - Prob. 55CRPCh. 11 - Prob. 56CRPCh. 11 - Prob. 57CRPCh. 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
Knowledge Booster
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, computer-science and related others by exploring similar questions and additional content below.Similar questions
- .. 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 = -1arrow_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. , (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_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_forward
- Consider 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_forwardDesign 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_forward
- How 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_forwardNo hand written and fast answer with explanationarrow_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) 0.1 ZI 0.3 0.9 W 0.7 W3 0.2 h W 0.55 0.4 W 0.55 W 0.5 h 0.4 W Z2 0.34 W, 0.2 Wo Figure 1arrow_forward
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Recommended textbooks for you
Database System ConceptsComputer ScienceISBN:9780078022159Author:Abraham Silberschatz Professor, Henry F. Korth, S. SudarshanPublisher:McGraw-Hill Education
Starting Out with Python (4th Edition)Computer ScienceISBN:9780134444321Author:Tony GaddisPublisher:PEARSON
Digital Fundamentals (11th Edition)Computer ScienceISBN:9780132737968Author:Thomas L. FloydPublisher:PEARSON
C How to Program (8th Edition)Computer ScienceISBN:9780133976892Author:Paul J. Deitel, Harvey DeitelPublisher:PEARSON
Database Systems: Design, Implementation, & Manag...Computer ScienceISBN:9781337627900Author:Carlos Coronel, Steven MorrisPublisher:Cengage Learning
Programmable Logic ControllersComputer ScienceISBN:9780073373843Author:Frank D. PetruzellaPublisher:McGraw-Hill Education
Database System Concepts
Computer Science
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:McGraw-Hill Education
Starting Out with Python (4th Edition)
Computer Science
ISBN:9780134444321
Author:Tony Gaddis
Publisher:PEARSON
Digital Fundamentals (11th Edition)
Computer Science
ISBN:9780132737968
Author:Thomas L. Floyd
Publisher:PEARSON
C How to Program (8th Edition)
Computer Science
ISBN:9780133976892
Author:Paul J. Deitel, Harvey Deitel
Publisher:PEARSON
Database Systems: Design, Implementation, & Manag...
Computer Science
ISBN:9781337627900
Author:Carlos Coronel, Steven Morris
Publisher:Cengage Learning
Programmable Logic Controllers
Computer Science
ISBN:9780073373843
Author:Frank D. Petruzella
Publisher:McGraw-Hill Education