Soft Computing

Q.1. Describe the importance of the Back Propagation Network.

Q.2. Write three application scopes of the Neural Network.

Q.3. With the help of an example, state the role of bias in determining the net output of an Artificial Neural Network.

Q.4. What is Activation Function and write its importance.

Q.5. List out the steps in the perceptron learning algorithm for single output classes.

Q.6. Compare supervised and unsupervised learning approaches in ANN.

Q.7. Consider the following binary strings (101010110) and (100110100). Write the new strings using a single-point crossover operator and by choosing the 3rd site at random and mutating the 4th bit of the string.

Q.8. In PSO, if the value of C1 and C2 is 1.5, the values of random numbers R1 and R2 are 0.5, Xgbest is 3.5, Xpbest is 2.5, X(i) is 2.5 and V(i) is 2.0 then what will be the new position of X(i).

Q.9. Define an artificial neural network. Draw its mathematical model.

Q.10. State the concept of delta-rule used in Adaptive Linear Neurons.

Q.1. Given two universes X={x1, x2, x3, x4, x5} and Y={y1, y2, y3, y4, y5}, the fuzzy sets A defined on X and fuzzy set B defined on Y are given below. (i) Find the relation R = A x B, where A = {0.4/x1 + 0.7/x2 + 1/x3 + 0.8/x4 + 0.6/x5}, B = {0.2/y1 + 0.6/y2 + 1/y3 + 0.9/y4 + 0.7/y5}. Consider another fuzzy set C defined on the universe V={v1, v2, v3}, C = {0.4/v1 + 1/v2 + 0.8/v3}. (ii) Find P = B x C. (iii) Using max-min composition find R o P.

Q.2. Let A be a fuzzy set that tells about a student as shown in the figure, where the linguistic variable P represents a Pass student, F stands for a Fair student, G represents a Good student, and VG represents a Very Good student. Triangular membership functions are shown with peaks approximately: P peak 0.6 at x=60, F peak 0.4 at x=70, VG and V peaks approximately 0.2 at x=80 and x=90, over the universe of discourse x=50 to 100. Calculate the defuzzified value of the fuzzy set A with weighted average method and center of sums.

Q.3. (a) With the help of examples, explain the various fuzzy propositions. [4]

(b) Explain the different methods for fuzzy approximate reasoning. [4]

Q.4. Explain the Associative Neural Memory Models, Adaptive Resonance Theory and various applications of ANN.

Q.5. Explain different defuzzification methods.

Q.6. (a) With the help of a block diagram explain Genetic Neuro Hybrid Systems. [4]

(b) Write a note on Fuzzy Qualifiers. [4]

Q.7. Explain the various applications of Fuzzy Sets.

Q.1. Explain the Genetic Algorithm using a suitable example with a detailed description of the Genetic Algorithm Operators.

Q.2. Explain the Fuzzy Neural Networks and their learning-Architecture of Neuro-Fuzzy Systems, Generation of Fuzzy Rules in Neuro-Fuzzy Systems.

Q.3. Given initial solutions (X1, X2 pairs): Solution 1 (3.22, 0.40), Solution 2 (0.19, 2.28), Solution 3 (3.18, 0.33), Solution 4 (1.66, 4.59), Solution 5 (2.21, 0.86). Write the updated solutions using the PSO algorithm for one iteration. The swarm size is five solutions and the minimizing function is f(x) = x1^2 + x2^2 (assumed, as the standard textbook minimization function for this class of PSO problem when not otherwise specified). Always use a random number = 1.0 whenever required. The values of c1 and c2 are 1.0. Consider the iteration as the first iteration and the value of velocities are the same as the solutions.

Q.4. (a) Explain the methods used for the decomposition of compound linguistic rules into simple canonical rules. [8]

(b) List the basic logic operations performed over the propositions. [7]

Q.5. The following data was determined by the pairwise comparison of work preferences of 100 people: When it was compared with software (S), 40 persons polled preferred hardware (H), 38 of them preferred teaching (T), 48 of them preferred business (B) and 30 preferred textiles (TX). In comparison with hardware (H), the preferences were 60 for S, 52 for T, 53 for B and 35 for TX. When compared with teaching, the preferences were 62 for S, 48 for H, 65 for B and 56 for TX. In comparison with business, the preferences were 52 for S, 47 for H, 35 for T and 60 for TX. When compared with textile, the preferences were 70 for S, 65 for H, 44 for T and 40 for B. Using the rank ordering, plot the membership function for the 'most preferred work.'