Consider the following table for the connections between input neurons and the hidden layer neurons:
| Input Neurons | Hidden Layer Neurons | Connection Weight |
| 1 | 1 | - 1 |
| 1 | 2 | - 0.1 |
| 1 | 3 | 1 |
| 2 | 1 | - 1 |
| 2 | 2 | 1 |
| 2 | 3 | 1 |
| 3 | 1 | - 0.2 |
| 3 | 2 | - 0.3 |
| 3 | 3 | - 0.6 |
The connection weights from the hidden layer neurons to the output neurons are -0.6, -0.3 and -0.6, 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.
The network consists of three input neurons connected to three hidden layer neurons, which in turn are connected to a single output neuron. Each input neuron is fully connected to each hidden neuron with the given weights. The ___ _________ _______ ____ __________ ___ _____.
____ __________ _____ ___ _________ ________.
____ ____ ___ ____ ________ ___ ___.
________ ____ _______ ___ _________ _____ _________ __________ _____ _______ _________.
___ ____ ________ __________ _____ _________ ________ _________ __________ _____ _____ _________.
_______ _______ _____ __________ __________ _____ _______ ___ ____.
__________ ___ _____ _______ ____ ____ _____ ___ _________ _________.
___ __________ ___ ___ ___ _______ _____ ______ ________ ________ ___.
________ _________ ___ ________ ___ _____ __________ _______ _________ ____.
_______ _________ _____ ____ _________ _____ __________ _________ _______ ____ ___.
____ ___ ____ ________ ______ ____ _____ _________ __________ ______ ______ ________.
__________ ______ ____ ____ ___ ______.
_____ __________ _____ ________ __________ _______.
_________ ______ ___ ________ ______ ___ __________ ______ ______.
__________ _________ __________ ____ ________ _______ _____.
__________ __________ _____ _____ _________ ________ _______ __________ ______.
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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.
Approximate the function for
, by solving 1-2-1 network, using Back propagation algorithm. The weighted structure and initial input are as follows:
Weighted structures are: and bias
and bias
The initial input is 1.
Draw the architecture of the model. Perform one iteration.
(क) के मान निकालिए, जहाँ C
से प्राप्त वक्र है।
(ख) द्विश: समाकलन का प्रयोग करके दीर्घवृत्तज
Determine the fuzzy relation T as a composition between the fuzzy relations R and S given below by using max-min and max-product:
(क) मान लीजिए
$
दिखाइए कि (0,0) पर सभी दिशाओं में f दिक् अवकलज होते हैं।
(ख) मान लीजिए और f, x और y का एक संततः अवकलनीय फलन है जिसके आंशिक अवकलज भी संततः अवकलनीय हैं। दिखाइए कि
$
(ग) मान लीजिए के तीन बिन्दु हैं।
|2b - a + 3c| ज्ञात कीजिए।
Determine the -cut of the fuzzy set (A) are given below, at 0.7 and 0.2.
Also, compare the -cut of the two outcomes, and give comments for status of
-value variation.
क) यदि तो a और b के मान ज्ञात कीजिए।
(ख) मान लीजिए कि S और C के उपसमुच्चय हैं। S मूल-बिन्दु पर केन्द्र वाला एकक विवृत गोलक है तथा C विवृत घन
।
निम्नलिखित में से कौनसा कथन सत्य है? अपने उत्तर की पुष्टि कीजिए।
(i)
(ii)
(ग) निम्नलिखित फलनों के स्तर वक्र ज्ञात कीजिए :
(i)
(ii)
(iii) x-y
(iv) y / x