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Register Number SATHYABAMA UNIVERSITY (Established under section 3 of UGC Act,1956) Course & Branch :B.Tech - IT Title of the Paper :Soft Computing Sub. Code :612702(2007-08) Date :24/08/2012 Max. Marks:80 Time : 3 Hours Session :FN _______________________________________________________________________________________________________________________________ 1. 2. PART - A (10 x 2 = 20) Answer ALL the Questions List three features which distinguish different types of artificial neural networks from each other. Which of the following neural networks uses supervised learning? Simple recurrent network. Self-organizing feature map. Hopfield network. All of the above answers. 3. Considering a graphical representation of the `tallness' of people using its appropriate member function, which of the following combinations are true? (a) TALL is usually the fuzzy subset. (b) HEIGHT is usually the fuzzy set. (c) PEOPLE is usually the universe of discourse. 4. Given these fuzzy graphs for member functions A and B. Graphically show the result of the operation A OR B. 5. If crossover between chromosomes in search space does not produce significantly different offspring, what does it imply? 6. The qualities of solutions offered by GAs for any problems are always better than those provided by other search. 7. Distinguish semantic nets from frames. 8. How does uncertainty affect reasoning in inference chains? 9. What sort of knowledge can be categorised as a sequence of instructions or commands such as those presented in a cooking recipe or motor car repair manual? 10. By what technique the facts from the knowledge base of an expert system to prove a given goal is established? PART – B Answer All the Questions (5 x 12 = 60) 11. Develop the delta learning rule for a multi-layer perceptron (using error back-propagation), which updates the weight wji joining neuron i to neuron j. Assume that the activation functions in the network are continuous. Consider cases of o o j is an output neuron j is a neuron in a hidden layer (or) 2 y wk xk k 0 12. An MLP is described by a single neuron as: where x0 = 1 and w0 corresponds to the bias term. If the function (.) is a threshold function (min. = 0, max = 1), show (graphically) the decision line implemented by the MLP and the values obtained for each of the input subspaces, where: w0 = -1 , w1 =1 , w2 = 1 13. Discuss the ANFIS model architecture with a neat diagram. (or) 14. Explain the operation of a Fuzzy Logic Controller with a simple example. 15. Design a genetic algorithm to solve the any problem of your choice, for eg., Travelling Sales Person problem. Describe precisely the bit-string encoding and a set of crossover operators. Then, given a data set, discuss the procedure of using genetic algorithm to derive the solution and also the stopping criterion. (or) 16. Consider the strings and schemata of length 11. For the following schemata calculate the probability of surviving mutation if the probability of mutation is 0.001 at a single bit position. **100****10, 0**********1, 11***00***1, *1111*0000*. Recalculate the survival probabilities for a mutation probability Pm=0.1. 17. What is wrong with the following argument: Men are widely distributed over the earth. Socrates is a man. Therefore, Socrates is widely distributed over the earth. How should the facts be represented by these sentences be represented in logic so that this problem does not rise? (or) 18. Construct partitioned semantic net representation for the following: (a) Every batsman hit a ball. (b) All the batsmen like the wicket-keeper. 19. How can the development of an expert system be viewed as a core software engineering process? Illustrate with an appropriate example to support your discussion. (or) 20. (a) Explain the detailed life cycle model of an expert system. (b) Discuss the features of the various components of a typical rule-based expert system.