a) Define Error Correction Learning with examples.
Error correction learning is a concept within soft computing that involves adjusting the parameters of a system in order to minimize the difference between the desired output and the actual output. It is a fundamental mechanism used in various machine learning algorithms, particularly in supervised ___ ______ _________ _______ ______ ____ _______ ___ ____ ______ _____.
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Which of the following statements are true or false? Give a short proof or a counter example in support of your answers.
a) There is chance of occurrence of the premature convergence in Roulett-wheel selection shceme used in GA.
b) Gradient based optimization methods are used when the objective function is not smooth and one needs efficient local optimization.
c) The cut of a fuzzy set A in U is defined as A
d) A single perception with preprocessing is neither an auto associative network nor a multiple layer neural network.
e) If W then perception is non-linear separable.
a) Define Error Correction Learning with examples.
Let A and B are two fuzzy sets and and
then find out the following membership values:
i) ii)
iii)
iv) v)
vi)
a) Consider the following travelling salesman problem involving 9 cities
| Parent 1 | G | J | H | F | E | D | B | I | C |
| Parent 2 | D | C | H | J | I | G | E | F | B |
i) Order crossover #1, assuming and
sites as the crossover sites
ii) Order crossover #2, assuming
and
as the key positions.
b) Describe the Function Approximation in MLP. Also, explain Generalization of MLP.
Consider a dataset of six points given in the following table, each of which has two features and
Assuming the values of the parameters c and m as 2 and the initial cluster centers
and
apply FCm algorithm to find the new cluster center after one iteration.
| 3 | 11 | |
| 3 | 10 |
| 8 | 12 | |
| 10 | 6 | |
| 13 | 6 | |
| 13 | 5 |
a) Two sensors based upon their detection levels and gain settings are compared. The following of gain setting and sensor detection levels with a standard item being monitored provides typical membership values to represent the detection levels for each of the sensors.
| Gain Setting | Sensor detection levels | Sensor 2 detection levels |
| 0 | 0 | 0 |
| 20 | 0.5 | 0.35 |
| 40 | 0.65 | 0.5 |
| 60 | 0.85 | 0.75 |
| 80 | 1 | 0.90 |
| 100 | 1 | 1 |
The universe of discourse is Find the membership function for the two sensors. Also, verify De-morgon’s laws for these membership functions.