Calculate the output of the neural network illustrated below using the following activation functions Bave W;=4 X1 ( 1 W;=3 X2 (1 W3=2 f(net) (net → y X3 ( 1 X4 1 a) logsig Activation function b) tansig Activation Function

By using neural network algorithm solve the XOR problem. Where X1 and X2 are inputs Y is the output. Learning rate is 0.5. Threshold is 1 if weighted sum >= 0.5 otherwise 0 and weights w1 and w2 = 0.9 Add 17 with learning rate and w1, w2 before start solving the question. X1 X2 Y 0 0 0 0 1 1 1 0 1 1 1 0

Consider a three-neuron, two-layer network shown in Fig 2. The activation function of the neurons are sigmoid and the learning factor assume to be = 0.1. Using the back-propagation training method to detennine: -12 Dumny neuns Cnpus) [1 2.2] [W30 Wa1 M2 w-W4e W W42-o -0.5 1.5 Wse Ws3 Ws 1.2 0.75 1.4 0-101 0]- (1 1] 2-1, d--1, n=0.1 The output of the of the neurons 3,4 and 5 are: O a 0.616 ,0.731, 0.670. Ob 0.716 ,0.731, 0.570 0.916 ,0.731, 0.670. Od 0.816 ,0.631, 0.570

Q3. The weights, activation function, and set of input values for a Neural Network are given below: Weights: wl = 2, w2 = -3, w3 = 4, w4 = -2, w5 = 5 1 when p > 0 O when p = 0 -1 when p <0 Set of input values (Set S1, S2, S3 and input values x1, x2, x3, x4, x5): Activation function: f (p): where p is weighted sum of inputs S1 S2 S3 x1 2 4 x2 3 2 3 x3 3 6 -2 x4 -4 -3 3 X5 -3 -2 3 What is the final output value for each set of input values?

(c) A fully connected neural network is given below. ● x1 x2 1 Note that: i. ii. iii. iv. w11 w21 W31 w12 w22 w32 al a2 1 ReLU RELU h12 h21 h11 h22 h31 h32 The ReLU activation function is defined as follows: For an input x, ReLU(x) = max(0, x) The Softmax function is defined as follows: y1 s (x₂) y2 = Given the inputs, xi, i=1, ..., n; the outputs are as follows: exi Calculate the net inputs al and a2. Calculate the ReLU outputs. Calculate y1 and y2. Calculate z1 and 22. Softmax exj Suppose that the weights of the network are w11=-1.5; w12=-1.0, w21-1.2; w22=1.0; w31=-0.6; w32=1.0 h11=-0.5; h12=-1.0; h21=1.0, h22=1.5; h31= 0.4; h32=0.3 For an input of x1= 1.0 and x2=2.0 z1 z2

MATLAB Computer Science

Artificial Neural Networks Question 1. A neuron 'k' receives input from other four neurons, as corresponding synaptic weights are 0.3, -0.5, -0.4 and 3.1 Assume the bias as +1. 5, 3, 4 and -1. The i. Draw the computational model of neuron 'k'. ii. Calculate the linear output of the neuron. iii. Find the output using Mc-Culloch Pitts model in activation function.