Define Kohonen networks with examples.
Kohonen networks, also known as Self-Organizing Maps (SOM), are a type of artificial neural network used for unsupervised learning. They are designed to produce a low-dimensional __________ _______ __________ ________ _____ _____ _________ ____.
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Construct the α − cut at α = 4.0 for the fuzzy sets defined in Q. 1(b).
Give an example of Radial Basis Function Network and draw its network diagram.
(0,0) पर निम्नलिखित फलन ƒ के सांतत्य और अवकलनीयता की जाँच कीजिए, जहाँ
अन्यथा
z = 1 और 2 = x² + y² से परिबद्ध ठोस घनाकृति का द्रव्यमान ज्ञात कीजिए, जबकि घनत्व फलन्, 8(x, y, z) = |x| हो।
निम्नलिखित के स्तब्ध बिन्दु निर्धारित करके उनका वर्गीकरण कीजिए :
(i)
(ii)
Consider a dataset of six points given in the following table, each of which has two features f1 and f2 . Assuming the values of the parameters c and m as 2 and the initial cluster centers v1 = (5,5 ) and v2 (10 ,10, ) , apply FCm algorithm to find the new cluster center after one iteration.
|
| f1 | f 2 |
| x1 | 3 | 11 |
| x 2 | 3 | 10 |
| x3 | 8 | 12 |
| x 4 | 10 | 6 |
| x5 | 13 | 6 |
| x 6 | 13 | 5 |
Consider a Kohonen self-organizing net (given below) with two cluster units and five input units. The weight vectors for the cluster units are given by
w1 = { 3.0,5.0,7.0,9.0,0.1 }
w2 = { 0.1,9.0,7.0,5.0,3.0 }
Use the square of the Euclidean distance to find the winning cluster unit for the input pattern (x)
x = [ 0.0 5.0 0.1 5.0 0.0 ]
Using learning rate of 0.25, find the new weights for the winning unit.