Computer the output for the neurons in the kohonen networks, the related data is given below:
i) Input to Kohnen neural network:
Input Neuron-1
Input Neuron-2
ii) Connected weights between the neurons are as given below:
For a Kohonen network, the output of each neuron is computed as the weighted sum of inputs. Given: Inputs: ( I_1 = 0.5, \quad I_2 = 0.75 _________ _______ ___ _________ _________ ________ _____ ____ __________.
_________ ________ ______ _________ ______ _______ ________.
__________ ______ _______ _____ _____ ____ __________ ___ _____ _____.
________ ________ _____ __________ _____ __________ ________ _______ ____ _________.
_________ ___ ___ ________ _______ ________ ______ ____ _________ ___ ____.
_____ ____ __________ _____ ____ ______ _______.
_______ __________ ____ _________ _____ ___ _____ ____ ____ _________ __________ _______.
________ _________ ___ ________ ______ ____ ______ __________ ______.
________ _________ ___ _______ _________ _______ ______ ___ _____ ________.
_______ ________ ________ __________ ____ _____.
_______ _________ ___ __________ ____ ________ ___ ___ ________ ______.
_________ ______ ____ _________ _____ __________ ________ ____ ____.
_________ _____ __________ ___ __________ ____ ___ _________ __________ _____ _______ ________.
___ _____ _________ _________ ________ ________ _____ __________.
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____ _____ ___ _________.
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State whether the following statements are True or False. Give short proof or a counter example in support of your answer.
a) In the Hopfield network, the neurons belonging to the same layer receive input from the neurons of the previous layer and send their value only to the neurons of the next layer.
b) The length of chromosomes to determine the maximum value of the set:
is 12.
c) The fuzzy relation (R) given below, is an equivalence relation.
d) The Self Organizing Map (SOM) is a supervised learning technique.
e) In a single layer neural network, if , then the output is -1.
Generate the population in the next iteration by using Roulette-Wheel criterion:
|
|
|
| 1 | 3.5 |
| 2 | 4.6 |
| 3 | 5 |
| 4 | 2.8 |
| 5 | 1.8 |
Using diagram, show the difference between feed-forward neural network and recurrent neural network.
A single layer neural network is to have six inputs and three outputs. The outputs are continuous over the range 0 to 1. Now answer the following:
i) How many neurons are required?
ii) What are the dimensions of the weight matrix?
iii) What kind of transfer function could be used?
iv) Is a bias required? Give reasons.
Consider the single layer perception given below:
The activation function is:
Obtain the output for each of the following input pattern:
| Patterns |
|
|
|
|
|
| 1 | 0 | 1 | 1 |
|
| 0 | 1 | 0 | 1 |
|
| 0 | 1 | 1 | 1 |
(क) क्या निम्नलिखित फलन श्वार्ज़-प्रमेय की आवश्यकताओं को (1,1) पर संतुष्ट करता है? अपने उत्तर की पुष्टि कीजिए। (4)
(ख) निम्नलिखित के स्तब्ध बिन्दु निर्धारित करके उनका वर्गीकरण कीजिए : (6)
(i)
(ii)
Verify whether the Genetic Algorithm (GA) improves the solution from one generation to the next generation, for the function given below:
Maximize:
Subject to:
Assume that chromosomes of length 6 are created at random and modified by Roulette-Wheel selection.