ARTIFICIAL NEURAL NETWORK HELP PLEASE
Compute the output value for the neural network shown below. The artificial neural network has two inputs, two neurons in the hidden layer 1, one neuron in the output layer and one output.
Suppose that the artificial neural network is using the logsig function
A). manually and B). using neural lab code in C
Answer should be z = 0.641199
Please answer BOTH A and B AND show FULL work
Homework Answers
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ARTIFICIAL NEURAL NETWORK HELP PLEASE Compute the output value for the neural network shown below. The artificial neural...
2. (20) Design an artificial neural network with two hidden layers. First hidden layer has s...
2. (20) Design an artificial neural network with two hidden layers. First hidden layer has s neurons, second hidden layer has 3 neurons. Input parameters are 3, output parameter i s (20) What is the fundemental philosophy in backpropagation training algorithm, Explain detail. 4 (30) Define the following terms and their effects on the performance of ANN. a) Learning factor b) Momentum factor. c) Number of hidden neuron d) Training data e) Initial Weights Target Output
1). The weight of w12 is damaged. Before this power failure the output of the network...
1). The weight of w12 is damaged. Before this power failure the
output of the network is 0.92129 when input x was applied. Compute
the value of w12 weight supposing that the activation function is
the logsig
(Ans: w12 = 7.5)
2). A power failure damaged weights w11 and w12. Before the
damage the output network was 0.539915 when the first column of x
was applied and 0.327393 for the second column of x. Compute the
values of w11 and...
1. Compared with PID Control, what are the advantages and disadvantages of Neural Network Control...
1. Compared with PID Control, what are the advantages and disadvantages of Neural Network Control? 2. The multi-layer neural network shown in Figure I has two inputs and one output. The network has two neurons in a hidden layer. The network is to be trained with backpropagation algorithm. Each neuron has a sigmoid activation function: Assume that the biases to the neurons is +1 and the learning rate is 1. The network has the following initial weights: (w). w1 wa1...
1. Consider a neural network, which contains one hidden layer and an output layer with one...
1. Consider a neural network, which contains one hidden layer and an output layer with one output unit. Let the hidden units have negative sigmoid as the activation function, which is formulated as 1 n(v) 1 + exp(-1) and the output unit has a linear activation function in which the output is equal to the activation input). (a) Show that the derivative of the negative sigmoid obeys the following relation dn(v) dv = n(v)(1 + n(v)) (b) Let the cost...
activation functions (ReLU) are in this network, before the final output? (Experimental) In a neural network with two internal layers and a total of 10 neurons, should you put more of those neuro...
activation functions (ReLU) are in this network, before the final output? (Experimental) In a neural network with two internal layers and a total of 10 neurons, should you put more of those neurons in layer 1 or layer 2? 10
activation functions (ReLU) are in this network, before the final output? (Experimental) In a neural network with two internal layers and a total of 10 neurons, should you put more of those neurons in layer 1 or layer 2? 10
A deep learning problem. The following matrices describing a neural network were uncovered by scientists. The...
A deep learning problem.
The following matrices describing a neural network were uncovered by scientists. The weights for the hidden layer are given in the matrix W[1] = [0 1] The bias for the hidden layer is given in the vector b[1] = [1] The weights for the output layer are given in the vector W[2] [8] 0 1 The biases for the output layer are 612] = -0.5 0.75 The input X is given in the vector X 1.25...
Information in the neural network diagram below has been provided to enable you answer the questions...
Information in the neural network diagram below has been provided to enable you answer the questions that follows. Hidden Layer Output Layer Input Layer 0.05 N3=? Input] =02 — Inputl =0.2 01 -0.015 0.2 0.01 02 conX 003: 0.01 -0.015 N-?.005 N4=? 0.05 - No? - N6=? 0.01 0.015 Input2 =0.9— (2 0.9 0.02 0.05 -0.01 5 N5=? Using logistic function, compute the output of the neurons in the hidden layer and output layer. (15pts.) i) Neuron3 (N3) ii) Neuron4...
Draw a fully connected neural network with 1 hidden layer where the number of units input, hidden layer, and output layer are 3, 2, 1, respectively. . (5+5+5+5) a. Show all the weight matrices a...
Draw a fully connected neural network with 1 hidden layer where the number of units input, hidden layer, and output layer are 3, 2, 1, respectively. . (5+5+5+5) a. Show all the weight matrices and their dimensions for this neural network. b. Label the network connections using the weight values (e.g., w12, w23). c. Total how many weights do you need to train in this neural network? . Explain supervised and unsupervised learning in your own words. (10)
Draw a...
Please use excel to solve. Any help would be greatly appreciated. A B E F G...
Please use excel to solve. Any
help would be greatly appreciated.
A B E F G 1 C D Changes in Consumer Price Index, 1960-2006 (Percent) 2 Year-Year Percent Change in Commodities CPI Year-Year Percent Change in Services CPI 3 4 5 6 7 8 Commodities% 0.9 Services% 3.4 0.6 1.7 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37...
An object of weight 1 N is falling vertically. The time vs. speed data can be...
An object of weight 1 N is falling vertically. The time vs. speed data can be found here. In this case the effect of air-drag cannot be neglected. Use your critical thinking to estimate the air-drag coefficient . Make sure you include the units in your answer. 0 0 0.1 0.9992 0.2 1.993 0.3 2.978 0.4 3.948 0.5 4.898 0.6 5.826 0.7 6.728 0.8 7.599 0.9 8.438 1 9.242 1.1 10.01 1.2 10.74 1.3 11.43 1.4 12.09 1.5 12.7 1.6 ...
2. (20) Design an artificial neural network with two hidden layers. First hidden layer has s neurons, second hidden layer has 3 neurons. Input parameters are 3, output parameter i s (20) What is the fundemental philosophy in backpropagation training algorithm, Explain detail. 4 (30) Define the following terms and their effects on the performance of ANN. a) Learning factor b) Momentum factor. c) Number of hidden neuron d) Training data e) Initial Weights Target Output
1). The weight of w12 is damaged. Before this power failure the
output of the network is 0.92129 when input x was applied. Compute
the value of w12 weight supposing that the activation function is
the logsig
(Ans: w12 = 7.5)
2). A power failure damaged weights w11 and w12. Before the
damage the output network was 0.539915 when the first column of x
was applied and 0.327393 for the second column of x. Compute the
values of w11 and...
1. Compared with PID Control, what are the advantages and disadvantages of Neural Network Control? 2. The multi-layer neural network shown in Figure I has two inputs and one output. The network has two neurons in a hidden layer. The network is to be trained with backpropagation algorithm. Each neuron has a sigmoid activation function: Assume that the biases to the neurons is +1 and the learning rate is 1. The network has the following initial weights: (w). w1 wa1...
1. Consider a neural network, which contains one hidden layer and an output layer with one output unit. Let the hidden units have negative sigmoid as the activation function, which is formulated as 1 n(v) 1 + exp(-1) and the output unit has a linear activation function in which the output is equal to the activation input). (a) Show that the derivative of the negative sigmoid obeys the following relation dn(v) dv = n(v)(1 + n(v)) (b) Let the cost...
activation functions (ReLU) are in this network, before the final output? (Experimental) In a neural network with two internal layers and a total of 10 neurons, should you put more of those neurons in layer 1 or layer 2? 10
activation functions (ReLU) are in this network, before the final output? (Experimental) In a neural network with two internal layers and a total of 10 neurons, should you put more of those neurons in layer 1 or layer 2? 10
A deep learning problem.
The following matrices describing a neural network were uncovered by scientists. The weights for the hidden layer are given in the matrix W[1] = [0 1] The bias for the hidden layer is given in the vector b[1] = [1] The weights for the output layer are given in the vector W[2] [8] 0 1 The biases for the output layer are 612] = -0.5 0.75 The input X is given in the vector X 1.25...
Information in the neural network diagram below has been provided to enable you answer the questions that follows. Hidden Layer Output Layer Input Layer 0.05 N3=? Input] =02 — Inputl =0.2 01 -0.015 0.2 0.01 02 conX 003: 0.01 -0.015 N-?.005 N4=? 0.05 - No? - N6=? 0.01 0.015 Input2 =0.9— (2 0.9 0.02 0.05 -0.01 5 N5=? Using logistic function, compute the output of the neurons in the hidden layer and output layer. (15pts.) i) Neuron3 (N3) ii) Neuron4...
Draw a fully connected neural network with 1 hidden layer where the number of units input, hidden layer, and output layer are 3, 2, 1, respectively. . (5+5+5+5) a. Show all the weight matrices and their dimensions for this neural network. b. Label the network connections using the weight values (e.g., w12, w23). c. Total how many weights do you need to train in this neural network? . Explain supervised and unsupervised learning in your own words. (10)
Draw a...
Please use excel to solve. Any
help would be greatly appreciated.
A B E F G 1 C D Changes in Consumer Price Index, 1960-2006 (Percent) 2 Year-Year Percent Change in Commodities CPI Year-Year Percent Change in Services CPI 3 4 5 6 7 8 Commodities% 0.9 Services% 3.4 0.6 1.7 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37...
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