Consider a Kohonen net (Self Organization Feature Map) with two cluster units and live input units. The weight vectors for the cluster units are:- W1= (1, 0.8, 0.6, 0.4, 2) and W2 = (0.5, 0.4, 0.6, 0.8, 1) Use the square of the Euclidean distance to find the winning cluster unit for the input Pattern x=(0.5, 1, 0.5, 0, 0). Using a learning rate of 0.2, find the new weights for the d winning unit.
Consider a Kohonen net (Self Organization Feature Map) with two cluster units and live input units. The weight vectors for the cluster units are:- W1= (1, 0.8, 0.6, 0.4, 2) and W2 = (0.5, 0.4, 0.6, 0.8, 1) Use the square of the Euclidean distance to find the winning cluster unit for the input Pattern x=(0.5, 1, 0.5, 0, 0). Using a learning rate of 0.2, find the new weights for the d winning unit.
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![0.6: Consider a Kohonen net (Self Organization Feature Map) with two cluster units and five
input units. The weight vectors for the cluster units are:-
W1 = (1, 0.8, 0.6, 0.4, 2)
and
W2 = (0.5, 0.4, 0.6, 0.8, 1)
Use the square of the Euclidean distance to find the winning cluster unit for the input
Pattern x=(0.5, 1, 0.5, 0, 0). Using a learning rate of 0.2, find the new weights for the
2
winning unit.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Ff46983df-c066-45cf-8428-1cb13b6abfb8%2F0aec5086-4d2c-49e2-ae87-b051f469dec0%2F40urfc_processed.jpeg&w=3840&q=75)
Transcribed Image Text:0.6: Consider a Kohonen net (Self Organization Feature Map) with two cluster units and five
input units. The weight vectors for the cluster units are:-
W1 = (1, 0.8, 0.6, 0.4, 2)
and
W2 = (0.5, 0.4, 0.6, 0.8, 1)
Use the square of the Euclidean distance to find the winning cluster unit for the input
Pattern x=(0.5, 1, 0.5, 0, 0). Using a learning rate of 0.2, find the new weights for the
2
winning unit.
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