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3. (E11.7) For the network shown in Figure 2 the initial weights and biases are chosen to be w1(0...
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...
4.7. Consider a two-layer feedforward ANN with two inputs a and b, one hidden unit c, and one output unit d. This net...
4.7. Consider a two-layer feedforward ANN with two inputs a and b, one hidden unit c, and one output unit d. This network has five weights (wca, Wcb, Wco, Wse, Wao). where wro represents the threshold weight for unit x. Initialize these weights to the values (.1,.1,.1,.1,.1), then give their values after each of the first two training iterations of the BACKPROPAGATION algorithm. Assume learning rte '-.3, momentum α-: 0.9, incremental weight updates, and the following training examples: 0 1...
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....
For a 2-D convolutional neural network in the following figure softmax Fully connected 4 Fully connected...
For a 2-D convolutional neural network in the following figure softmax Fully connected 4 Fully connected 128 Max pooling 2x2, stride 2x2 Conv kernal 4x4, stride 2x2, filter 32, valid padding Conv kernal 8x8, stride 4x4, filter 16, valid padding Input 84x84x1 (a) How many weights and biases are there in this neural network? Please specify the number of weights and biases for each layer respectively. (b) Please describe the shapes of the outputs of each layer. (e.g. for input...
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...
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
Artificial Neural Network Using the Perceptron learning rule, Find the weights required to perform the following...
Artificial Neural Network
Using the Perceptron learning rule, Find the weights required to perform the following classifications: 1. Vectors (1,1,1,1) and (0,1,0,0) are members of the class, and therefore have target value 1. 2. Vectors (1,1,1,0) and (1,0,0,1) are not members of the class, and have target value 0. Use a learning rate of 1, 0 =0.3, and starting weights of 0. Using each of the training vectors as input, test the responses of the net.
Consider a pair of sink and source as shown in the figure. The strength of both sink and source are -3 and 3 m2/s....
Consider a pair of sink and source as shown in the figure. The strength of both sink and source are -3 and 3 m2/s. 02 m 0.2 m (2) (1) Show that the stream function that pass through a point with radius r and angle 0 from orig is given by 3 tan-1 (0.4r sin e 2m tan a-tan B Given that tan(a - B) = r2-0.04 1+tan a tan B
Consider a pair of sink and source as shown...
Q6: In the circuit shown in Figure-6, assume the initial conditions, i0-0 and v(0)-0.The input, Vin...
Q6: In the circuit shown in Figure-6, assume the initial conditions, i0-0 and v(0)-0.The input, Vin is a step voltage of 5V at t20. (i Draw the Laplace Transformed circuit of the network at t20. (i Find Laplace function, Vou s). (iii) Solve for Vourt) [61 2? 4? Vin-5u(t) 0.2F 0.5H v out Figure-6 4
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...
4.7. Consider a two-layer feedforward ANN with two inputs a and b, one hidden unit c, and one output unit d. This network has five weights (wca, Wcb, Wco, Wse, Wao). where wro represents the threshold weight for unit x. Initialize these weights to the values (.1,.1,.1,.1,.1), then give their values after each of the first two training iterations of the BACKPROPAGATION algorithm. Assume learning rte '-.3, momentum α-: 0.9, incremental weight updates, and the following training examples: 0 1...
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....
For a 2-D convolutional neural network in the following figure softmax Fully connected 4 Fully connected 128 Max pooling 2x2, stride 2x2 Conv kernal 4x4, stride 2x2, filter 32, valid padding Conv kernal 8x8, stride 4x4, filter 16, valid padding Input 84x84x1 (a) How many weights and biases are there in this neural network? Please specify the number of weights and biases for each layer respectively. (b) Please describe the shapes of the outputs of each layer. (e.g. for input...
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...
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
Artificial Neural Network
Using the Perceptron learning rule, Find the weights required to perform the following classifications: 1. Vectors (1,1,1,1) and (0,1,0,0) are members of the class, and therefore have target value 1. 2. Vectors (1,1,1,0) and (1,0,0,1) are not members of the class, and have target value 0. Use a learning rate of 1, 0 =0.3, and starting weights of 0. Using each of the training vectors as input, test the responses of the net.
Consider a pair of sink and source as shown in the figure. The strength of both sink and source are -3 and 3 m2/s. 02 m 0.2 m (2) (1) Show that the stream function that pass through a point with radius r and angle 0 from orig is given by 3 tan-1 (0.4r sin e 2m tan a-tan B Given that tan(a - B) = r2-0.04 1+tan a tan B
Consider a pair of sink and source as shown...
Q6: In the circuit shown in Figure-6, assume the initial conditions, i0-0 and v(0)-0.The input, Vin is a step voltage of 5V at t20. (i Draw the Laplace Transformed circuit of the network at t20. (i Find Laplace function, Vou s). (iii) Solve for Vourt) [61 2? 4? Vin-5u(t) 0.2F 0.5H v out Figure-6 4
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