Consider a dataset of five observations given in the following table, each of which has two features f and f 2:
Assume the number of cluster c = 3 and the real number m = 2 . Also, assume the initial cluster centers as V )1,1( 1 = and V )2,2( 2 = . Apply fuzzy c-mean algorithm to find the modified cluster center after one iteration.
To apply the Fuzzy C-Means (FCM) algorithm, we will follow the steps outlined below.
Step 1: Initialize the Parameters
Given the dataset of five observations with two features f1f_1 and f2f_2, and the initial cluster centers V1=(1,1)V_1 = (1, 1) and V2=(2,2)V_2 = (2, 2), _________ ______ _______ ______ __________ ________ _____ ________ _____.
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Solve the network to approximate the function:
g(x)=1+sin
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and
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i) Ac ,Bc
ii) A ∪B
iii) A ∩B
iv) (A ∪B)c
v) (A∩B)c
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The connection weights from the hidden layer neurons to the output neurons are
− ,6.0 − 3.0 and − ,6.0 for the first, second and third neurons, respectively.
Corresponding threshold value for the output layer is 0.5 and for the hidden layer is 1.8, 0.005 and 0.2 for the first, second and third neurons, respectively.
i) Draw the diagram of the network.
ii) Write the output at each node.
c) Using diagram, show the difference between feed-forward neural network and
recurrent neural network.
Consider a dataset of five observations given in the following table, each of which has two features f and f 2:
Assume the number of cluster c = 3 and the real number m = 2 . Also, assume the initial cluster centers as V )1,1( 1 = and V )2,2( 2 = . Apply fuzzy c-mean algorithm to find the modified cluster center after one iteration.
Determine the α − cut of the fuzzy set (A) are given below, at 0.7 and 0.2.
Also, compare the α − cut of the two outcomes, and give comments for status of α − value variation.