SOFT COMPUTING (SC) [ELECTIVE], Semester 7, B.E. Computer Science (CS), December 2013.

SOFT COMPUTING (SC) [ELECTIVE], Semester 7,

B.E. Computer Science (CS), December 2013.

Con.7749-13

(REVISED COURSE)

LJ- 13927

(3 Hours)

[Total Mark: 100]

N. B.: (1) Question no 1 is compulsory.

(2) Attempt any four questions out of the remaining questions.

(3) Figures to the right indicate full marks.

1. (a) Model the following as a fuzzy set using suitable membership function – “Numbers close to 6”. ------ (8 Marks)

(b) Explain standard fuzzy membership functions. ---- (6 Marks)

(c) Determine all α – level sets as and strong α – level sets for the following fuzzy set. ---- (6 Marks)

A = { (1, 0.2) (2, 0.5) (3, 0.8) (4, 1) (5, 0.7) (6, 0.3) }

2. Design a fuzzy Controller to determine the wash time of a domestic washing machine. Assume that the inputs are dirt and grease on the clothes. Use three descriptors for each input variable and five descriptors for out put variable. Derive a set of rules for control action and defuzzification. The design should be supported by figures wherever possible. Clearly indicate that if the clothes are soiled to a larger degree the wash time required will be more. --- (20 Marks)

3. (a) Determine the weights after four steps of training for Perceptron learning rule of a single neuron network starting with initial weights: --- (10 Marks)

W = [0 0]^t , inputs as X1= [2 2]^t ,
X2 = [1 - 2]^t , X3 = [-2 2]^t , X4 = [-1 1]^t ,
d1 = 0, d2 = 1, d3 = 0, d4 = 1, and c = 1.

(b) Explain Mamdani type of Fuzzy Inference system in detail. ---- (10 Marks)

4. (a) Prove the following identities : - --- (10 Marks)

(i) For unipolar continuous activation function: -
f¹ (net) = 0 (1- 0)

(ii) For bipolar continuous activation function: -
f¹ (net) = 1/2(1- 0¹)

(b) Explain error back propagation training algorithm with the help of a flowchart. --- (10 Marks)

5. (a) Explain RBF network and give the comparison between RBF and MLP. --- (10 Marks)

(b) Explain with examples linearly and non – linearly separable pattern classification. --- (10 Marks)

6. (a) What is learning in neural network ? Differentiate between Supervised and Unsupervised Learning. ---- (10 Marks)

(b) Explain Travelling salesperson problem using simulated annealing. ---- (10 Marks)

7. Write notes on any two of the following: ---- (20 Marks)

(a) Learning Vector Quantization.

(b) Derivative Free Optimization.

(c) Winner takes all learning rule.

SOFT COMPUTING (SC) [ELECTIVE], Semester 7, B.E. Computer Science (CS), May 2013.

SOFT COMPUTING (SC) [ELECTIVE], Semester 7,

B.E. Computer Science (CS), May 2013.

Con.9787-13

(REVISED COURSE)

GS- 6157

(3 Hours)

[Total Mark: 100]

N. B.: (1) Question no 1 is compulsory.

(2) Attempt any four questions out of the remaining questions.

(3) Figures to the right indicate full marks.

1. (a) Explain cylindrical extension and projection operations on fuzzy relation with example. ---- (5 Marks)

(b) A neuron with 3 inputs has the weight vector w = [0.1 0.3 -0.2]. The activation function is binary sigmoidal activation function. If input vector is [0.8 0.6 0.4] then find the output of neuron. --- (5 Marks)

(c) What are the various types of Mutation techniques? ---- (5 Marks)

(d) Model the following as fuzzy set using trapezoidal membership function. --- (5 Marks)
“Middle age”

2.(a) What is supervised and unsupervised learning ? Compare different learning rules. ---- (10 Marks)

(b) Explain the Radial Basis function neural network for the solution of XOR. --- (10 Marks)

3. (a) Explain Kohonen’s self organizing neural network. --- (10 Marks)

(b) For the given membership function as shown in figure below, determine the defuzzified output value by 2 methods.---- (10 Marks)

4. (a) Explain the operations of genetic programming with help of flowchart . ---- (10 Marks)

(b) Compare Mamdani and Sugeno Fuzzy models. ---- (10 Marks)

5. Design fuzzy logic controller for water purification plant. Assume the grade of water and temperature of waters as the inputs and the required amount of purifier as the output. Use three descriptor for input and output variables. Derive set of rules for control the action and defuzzification. The design should be supported by figures. Clearly indicate that if water temperature is low and grade of water is low, then amount of purifier required is large. ---- (20 Marks)

6. (a) Explain Travelling salesperson problem using simulated Annealing. --- (10 Marks)

(b) Explain Error back propagation training Algorithm (EBPTA). ---- (10 Marks)

7. Write short notes on any two: --- (20 Marks)

(a) Character recognition using neural network

(b) Learning vector quantization

(c) Derivative based optimization.

SOFT COMPUTING (SC) [ELECTIVE], Semester 7, B.E. Computer Science (CS), December 2012.

SOFT COMPUTING (SC) [ELECTIVE], Semester 7,

B.E. Computer Science (CS), December 2012.

Con.10900-12

(REVISED COURSE)

KR- 1899

(3 Hours)

[Total Mark: 100]

N. B.: (1) Question no 1 is compulsory.

(2) Attempt any four questions out of the remaining questions.

1. (a) Model the following as Fuzzy set using suitable membership function “number close to 6”. ---- (5 Marks)

(b) Draw CANFIS architecture for Sugeno Fuzzy Model. ---- (5 Marks)

(c) Explain the different Fuzzy membership function. ---- (5 Marks)

(d) Explain Single Continues perceptron training Algorithm (SCPTA). ---- (5 Marks)

2. Design a Fuzzy controller to determine the wash time of domestic washing. Assume that input is dirt and grease on clothes. Use three descriptor for input variables. Derive set of rules for control the action and defuzzification. The design should be supported by the figures. Show that if the clothes are soiled to a larger degree the wash time will be more and vice- versa. ---- (20 Marks)

3. (a) Prove the following identities: ---- (10 Marks)

(i) For unipolar continuous activation function f ‘ (net) = O(1 - O)

(ii) For bipolar continuous activation function f ‘ (net) = O(1- O²) / 2
Where O is out.

(b) Explain Genetic Algorithm with the help of example. --- (10 Marks)

4. (a) Explain Error back propagation training algorithm with the help of a flowchart. ---- (10 Marks)

(b) Explain any four defuzzification methods with suitable Example. ---- (10 Marks)

5. (a) Explain with an example McCulloch – pitts neuron model. --- (6 Marks)

(b) Explain with neat diagram supervised and unsupervised learning. --- (6 Marks)

(c) State the different properties of Fuzzy set. ---- (8 Marks)

6. (a) Describe the basic Hopfield model and give the theory of energy Minimization in auto- associative Hopfield network. ---- (10 Marks)

(b) Explain the architecture of Bidirectional associative memory. Hoe is stored and retrieval performed in BAM? ---- (10 Marks)

7. Write short notes on any two of the following: ----- (20 Marks)

(a) TSP using simulated Annealing

(b) RBF network

(c) Multicategory perception training algorithm. (MCTPA).

SOFT COMPUTING (SC) [ELECTIVE], Semester 7, B.E. Computer Science (CS), May 2012.

SOFT COMPUTING (SC) [ELECTIVE], Semester 7,

B.E. Computer Science (CS), May 2012.

Con.4679-12

GN- 9047

(3 Hours)

[Total Mark: 100]

N.B. 1. Question No. 1 is compulsory.

2. Attempt any Four questions out of the remaining.

3. Figures to the right indicate full marks

1. (a) Explain fuzzy extension principle with the help of an example. --- (6 Marks)

(b) Explain Mc Culloch Pitts Neuron Model with help of an example. --- (6 Marks)

(c) Explain standard fuzzy membership functions. --- (8 Marks)

2. Design a Fuzzy logic controller for a domestic washing machine with two Inputs dirtiness of the load and weight of the laundry and output as amount of detergent used. Use five descriptors for each linguistic variable. Generate a set of rules for control action and defuzzification. ---- (20 Marks)

3. (a) What is learning in Neural Networks? Compare different learning rules. ---- (10 Marks)

(b) Explain error back propagation training algorithm with the help of a flowchart. ---- (10 Marks)

4. (a) Determine the weights after one iteration for hebbian learning of a single neuron network starting with initial weights W = [1, -1], inputs as ----- (12 Marks)

X1 = [1, -2], X2 = [2, 3], X3 = [1, -1] and c=1.

Use (i) Bipolar binary activation function

(ii) Bipolar continuous activation function

(b) Explain perceptron Learning with the help of an example. --- (8 Marks)

5. (a) Explain with examples linearly separable and non – linearly separable pattern classification. --- (10 Marks)

(b) Explain the three types of fuzzy Interface Systems in detail. --- (10 Marks)

6. (a) Explain Travelling Salesperson Problem using Simulated Annealing. ---- (10 Marks)

(b) Explain RBF network and give the comparison between RBF and MLP. --- (10 Marks)

7. Write notes on any two of the following ----- (20 Marks)

(a) Derivative based Optimization method of steepest descent.

(b) Learning Vector Quantization

(c) ANFIS Application – Printed Character Recognition

(d) Kohonen’s Self Organizing Network.

SOFT COMPUTING (SC) [ELECTIVE], Semester 7, B.E. Computer Science (CS), December 2011.

SOFT COMPUTING (SC) [ELECTIVE], Semester 7,

B.E. Computer Science (CS), December 2011.

Con.6873-11

MP- 5602

(3 Hours)

[Total Mark: 100]

N.B. 1. Question No. 1 is compulsory

2. Attempt any Four out of remaining

3. Assume suitable data if necessary and justify the assumptions

4. Figures to the right indicate full marks

1. (a) Explain Mc cul loch Pitts Neuron Model with help of an example. --- (5 Marks)

(b) Define support, Core, Normality, Crossover point and α – cut for fuzzy set. --- (5 Marks)

(c) A neuron with 4 inputs has the weight vector w = [1 2 3 4]^t. The activation function is linear, that is, the activation function is given by f (net) = 2* net. If the input vector is X == [5 6 7 8]^t, then find the output of the neuron. ---- (5 Marks)

(d) Explain Fuzzy Extension principle with example. --- (5 Marks)

2. (a) High speed rail monitoring devices sometimes make use of sensitive sensors to measure the deflection of the earth when a rail car passes. These deflections are measured with respect to some distance from the rail car and, hence are actually very small angles measured in micro radians. Let a universe of deflection be A = [1, 2, 3, 4] where A is the angle in mieroradians, and let a universe of distances be D == [1, 2, 5, 7] where D is distance in feet, suppose a relation between these two parameters has been determined as follows: --- (10 Marks)

Now let a universe of rail car weights be W = [1, 2], where W is the weight in units of 100,000 pounds. Suppose the fuzzy relation of W to A is given by

Using these two relations, tind the relation R To S = T

1. Using max-min composition
2. Using max-product composition

(b) What is learning? Compare different learning rules. ---- (10 Marks)

3. (a) Explain Error back propagation training algorithm with the help of a flowchart. --- (10 Marks)

(b) Explain Genetic algorithm with the help of example. ---- (10 Marks)

4. (a) Explain Random Search method with example. ---- (10 Marks)

(b) A single neuron network using f(net) = sgn(net) has been trained using the pairs of (Xi, di) as given below : --- (10 Marks)

X1 = [1 -2 3 -1] ^t, d1 = -1
X2 = [0 -1 2 -1] ^t, d2 = 1
X3 = [-2 0 -3 -1] ^t, d3 = -1
The final weights obtained using the perception rule are
W4 = [3 2 6 1] ^t
Knowing that correction has been performed in each step for c=1, determine the following weights :

(a) W3,W2, W1 by backtracking the training.

(b) W5, W6, W7, obtained for steps 4,5,6 of training by reusing the sequence
(X1. d1), (X2, d2), (X3, d3)

5. (a) Explain with example perceptron learning rule. ---- (10 Marks)

(b) Explain with example gradient based optimization technique. ---- (10 Marks)

6. Design a fuzzy logic controller for a train approaching or leaving a station. The inputs are the distance from the station and speed of the train. The output is the amount of break power used. Use four descriptors for each variable use Mamdani Fuzzy model. ---- (20 Marks)

7. Write short notes on any two of the following: --- (20 Marks)

(a) TSP using simulated Annealing

(b) Kohonen's self organizing network

(c) Character Recognition using neural network

(d) RBF network

SOFT COMPUTING (SC) [ELECTIVE], Semester 7, B.E. Computer Science (CS), December 2010.

SOFT COMPUTING (SC) [ELECTIVE], Semester 7,

B.E. Computer Science (CS), December 2010.

Con.6652-10

GT- 8850

(3 Hours)

[Total Mark: 100]

N. B.: (1) Question no 1 is compulsory.

(2) Attempt any four questions out of the remaining questions.

1. (a) Explain support and core of fuzzy set with examples. --- (5 Marks)

(b) Explain linearly separable and non-linearly separable patterns with examples. --- (5 Marks)

(c) Draw CANFIS architecture for Sugeno fuzzy model. --- (5 Marks)

(d) Model the following as fuzzy set using trapezoidal membership function : --- (5 Marks)
“Number close to 10”

2. (a) Explain Errorback Propogation Algorithm with the help of flowchart. --- (12 Marks)

(b) Explain ADALINE (Adaptive Linear Element). --- (8 Marks)

3. Using Mamdani fuzzy model, Design a fuzzy logic controller to determine the wash time of domestic washing machine. Assume that the inputs are dirt and grease on cloths. Use three descriptors for each input variable and five descriptors for the output variable. Derive a set of rules for control action and defuzzification. The design should be supported by figures wherever possible. --- (20 Marks)

4. (a) Give the basic steps involved in simulated Annealing method. Explain how ‘Travelling Salesperson Problem(TSP)’ can be solved using simulated Annealing method. --- (12 Marks)

(b) Determine the weights after one iteration for Hebbian learning of single neuron network starting with initial weight vector. --- (8 Marks)

inputs as

and C = 1. Use signum (bipolar binary activation function.

5. (a) Explain the major components of genetic algorithm. Give a simple genetic algorithm for maximization problem. --- (10 Marks)

(b) Let A = { a1, a2 }, B = { b1, b2, b3 }, C = { c1, c2 } ----- (10 Marks)

Let R be a relation from A to B defined by matrix: -

Let S be a relation from B to C defined by matrix: -

Find (i) Max – min composition of R and S.

(ii) Max product composition of R and S.

6. (a) Explain ANFIS architecture with neat diagram. ---- (10 Marks)

(b) Explain perception convergence theorem for single layer perception. ---- (10 Marks)

7. Write short notes on any four of the following: --- (20 Marks)

(a) Radial basis function network

(b) Printed character recognition

(c) Inverse kinematics problem

(d) Gradient descent method

(e) Kohonen self organizing network.