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- Artificial Neural Networks (Hebb Network): Using Hebb Network, train and update the weights. i-) Compute the latest weights and b. ii-) Compute Yin and Y for the given test vector. iii-) Write the decision of Hebb Network.Write a Matlab code to implement a neural network that will perform the functionality of a 3-input X-nor gate. Required items are: 1- Code. 2- A figure of the architecture of the neural network. 3- A table of the weights and bias of all perceptrons. 4- A testing that include all the cases (show the neural network output).Develop a neural network model which can perform XOR operation using McCulloch and Pitt’s Neural Network model.
- 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?.. 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 = -12. LSTM Bookmark this page The diagram below shows a single LSTM unit that consists of Input, Output, and Forget gates The behavior of such a unit as a recurrent neural network is specified by a set of update equations. These equations define how the gates, "memory cell" and the "visible state" h, are updated in response to input ar, and previous states - hr-1. For the LSTM unit, sigmoid (Whi+Wa+by) sigmoid (Whe+W+b) sigmoid (Wh1+W*+b) =f1+ tanh (W+Wa+b) tank (c) where symbol Ⓒ stands for element-wise multiplication. The adjustable parameters in this unit are matrices WWE W W W W W We, as well as the offset parameter vectors by, b,, b, and b. By changing these parameters, we change how the unit evolves as a function of inputs ar To keep things simple, in this problem we assume that C, and h are all scalars. Concretely, suppose that the parameters are given by =0 by-100 =-100 100 b 100 =50 We Calculate the values at each time-step and enter them below as an array [ho, ha, ha, ha, ha.hs…
- Error in prediction is used to determine the level of adjustment of weights for the next iteration in neural networks True False2: Back propagation: Consider the following neural network. Single-circled nodes denote variables (e.g. æ1 is an input variable, hy is an intermediate variable, y is an output variable), and double-circled nodes denote functions (e.g. Σ takes the sum of its inputs, and ♂ denotes the logistic function σ(x) = 1+e-x. In the network below, hi = l+e-T1w1T2W2 · 1 1 W1 = -1.7 Cloon Σ W2 = 0.1 1 h - 0.6 oblood Σ S2 W4 = - 1.8 W5 = ww 0.2 = 0.5 3Choose the best answer A perceptron is: a single layer feed-forward neural network with preprocessing an autoassociative neural network A perceptron consists of input values, weights and a bias, a weighted sum and activation D) Both A and C
- Consider a neuron with a sigmoidal activation function. The input vector [x1,x2]=[1,1] and the weights are [0.5,0.3]. The bias term is 0.2. What will be the output of the neuron? Give your answer correct to 2 decimal places after rounding (e.g. 0.45) Answer:Which of the following statements are usually true about the Learning rate in Neural Networks? (1) It may be defined as a factor that decides the amount by which the weights are updated. (2) If the learning rate is set too low, then the weight changes may be so big that the optimization may overshoot the minima and increase the error. (3) If the learning rate is set too high, training will progress very slowly as very tiny updates are made to the weights in the network. All the statements are true (2) & (3) (1) & (2) Only (1)Find the output value in the artificial neural network given below.