Implement AND function using McCulloch-Pitts neuron.
Sure thing! Implementing the AND function using McCulloch-Pitts neuron involves creating a logical model that mimics the behavior of an AND gate. The McCulloch-Pitts neuron is a simple model of a biological neuron, with binary inputs and outputs.
Let's consider two binary inputs, _______ ___ ___ ____ ___ ___ ________ _______ ________ ______ __________.
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______ _______ ___ _____ ________ _________.
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_____ ______ _____ __________ ________ __________ _________ _______ _______ ______ _________ ___.
_______ ______ ________ _________ __________ _________ ______ ______ _________ ______.
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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` (I1) =0.5
Input Neuron-2 (I2) =0.75
ii) Connected weights between the neurons are as given below:
I O 1.0:
1 → 1
I O 2.0:
2 → 2
I O 3.0:
1 → 2
I O 4.0
Let A and B be two Fuzzy sets as given below:
Determine the following:
i) Universe of discourse for Set A and Set B.
ii) Complements of Set A and Set B
iii) A ∩B
iv) A∪B
Consider the fuzzy sets A and B defined on the interval ,0[ ].5 Their membership functions are:
and
Determine the membership function and graph them for each of the following:
i) Ac ,Bc
ii) A ∪B
iii) A ∩B
iv) (A ∪B)c
v) (A∩B)c
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.
Find the length and order of the following schema:
i) S1 =1** 0 0 *1* *
ii) S2 = *0 0 *1 * *
iii) S * * * 0 * * * *
iv) S4 = *1 * 0 1 *
Consider the single layer perception given below:
Obtain the output for each of the following input pattern:
| Patterns | p1 | p2 | p3 | p4 |
| x1 | 1 | 0 | 1 | 1 |
| x2 | 0 | 1 | 0 | 1 |
| x3 | 0 | 1 | 1 | 1 |
Consider the following table for the connections between input neurons and the hidden layer neurons:
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.