Homework Answers
Q1. Ans: Advantages of neural network control are as follows:
- The algoritm and structure of this controller are simple
- we can apply it at any online real time to control it.
- it can be applied at industrial process also to take the advantage
- PID controller are more robust
- Any type of mathematical model is not compulsary
- rapid adjustment with shorter time
Disadvantages of neural network over PID:
- PID give less iteration
- PID derive slightly output
- it gives response late
- it is not stable at all the time
- recovery rate is poor
- if load disturbance happen then process is not take place
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1. Compared with PID Control, what are the advantages and disadvantages of Neural Network Control...
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
3. (E11.7) For the network shown in Figure 2 the initial weights and biases are chosen to be w1(0...
3. (E11.7) For the network shown in Figure 2 the initial weights and biases are chosen to be w1(0)-1, b (0) 1, w (0)2, ba (0) 1 An input/target pair is given to be Inputs Tan-Sigmoid Layer Tan-Sigmoid Layer Perform one iteration of backpropagation with a 1.
3. (E11.7) For the network shown in Figure 2 the initial weights and biases are chosen to be w1(0)-1, b (0) 1, w (0)2, ba (0) 1 An input/target pair is given to...
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...
5. (10 points) Optimization in neural network Consider a very simple neural network with two input...
5. (10 points) Optimization in neural network Consider a very simple neural network with two input values, one output value, and a single neuron with sigmoid activation. Each input to the neuron has an associated weight, and the neuron has a bias. So the network represents functions of the form o(W121 + W222 +b). We train the neural network using least squares loss on a single piece of training data ((1, -1),0). Initially all weights and biases are set to...
Exercise Optimization in neural network Consider a very simple neural network with two input values, one...
Exercise Optimization in neural network Consider a very simple neural network with two input values, one output value, and a single neuron with sigmoid activation. Each input to the neuron has an associated weight, and the neuron has a bias. So the network represents functions of the form o(W1X1 + W222 + b). We train the neural network using least squares loss on a single piece of training data ((1, -1),0). Initially all weights and biases are set to 1....
ARTIFICIAL NEURAL NETWORK HELP PLEASE Compute the output value for the neural network shown below. The artificial neural...
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
LAYER 1 LAYER 2 Neo...
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...
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...
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
3. (E11.7) For the network shown in Figure 2 the initial weights and biases are chosen to be w1(0)-1, b (0) 1, w (0)2, ba (0) 1 An input/target pair is given to be Inputs Tan-Sigmoid Layer Tan-Sigmoid Layer Perform one iteration of backpropagation with a 1.
3. (E11.7) For the network shown in Figure 2 the initial weights and biases are chosen to be w1(0)-1, b (0) 1, w (0)2, ba (0) 1 An input/target pair is given to...
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...
5. (10 points) Optimization in neural network Consider a very simple neural network with two input values, one output value, and a single neuron with sigmoid activation. Each input to the neuron has an associated weight, and the neuron has a bias. So the network represents functions of the form o(W121 + W222 +b). We train the neural network using least squares loss on a single piece of training data ((1, -1),0). Initially all weights and biases are set to...
Exercise Optimization in neural network Consider a very simple neural network with two input values, one output value, and a single neuron with sigmoid activation. Each input to the neuron has an associated weight, and the neuron has a bias. So the network represents functions of the form o(W1X1 + W222 + b). We train the neural network using least squares loss on a single piece of training data ((1, -1),0). Initially all weights and biases are set to 1....
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
LAYER 1 LAYER 2 Neo...
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...
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...
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