Neural Networks and Fuzzy Systems : Question Paper Dec 2014 - Electronics Engineering (Semester 8) | Mumbai University (MU)

Neural Networks and Fuzzy Systems - Dec 2014

Electronics Engineering (Semester 8)

(1) Question 1 is compulsory.
(2) Attempt any four from the remaining questions.
(3) Assume data wherever required.
(4) Figures to the right indicate full marks.
1 (a) What do you mean by learning and list different learning rule.(5 marks) 1 (b) Explain Hebbian learning rule.(5 marks) 1 (c) Explain Fuzzification and defuzzitication process.(5 marks) 1 (d) What are the salient features of Kohonen's self organizing learning algorithm.(5 marks) 2 (a) What are the learning strategies in RBF(10 marks) 2 (b) Explain perceptron learning rule convergence theorem(10 marks) 3 (a) Explain different fuzzy membership function.(10 marks) 3 (b) What are the learning factors of back propagation algorithm.(10 marks) 4 (a) What is the Hopfield model of neural network ? Explain its algorithm and differentiate discrete and continuous Hopfield model in terms of energy landscape and stable state.(10 marks) 4 (b) i) Compare RBF and MLP
(ii) How do you achieve fast learning in ART 2 network.
(10 marks)
5 (a) Perform two training steps of the network using delta learning rule of λ=1 and c=0.25. Train the network using following data pairs.
$$\left ( x_{1}=\begin{bmatrix} 2\\0 \\-1 \end{bmatrix}d_{1}=-1 \ltbr\gt Use f(net)=1/0(1-0\ltsup\gt2\lt/sup\gt) \right ), \left ( x_{2} -\begin{bmatrix} 1\\-2 \\-1 \end{bmatrix},d_{2}-1\right )$$ The initial weight are w1=[101]t
(10 marks)
5 (b) Find max-min composition and max-product composition.
$$R=\begin{bmatrix} 0.8 &0.1 &0.1 &0.7 \\0 &0.8 &0 &0 \\0.9 &1 &0.7 &0.8 \end{bmatrix} S=\begin{bmatrix} 0.4 &0.9 &0.3 \\0 &0.4 &0 \\0.9 &0.5 &0.8 \\0.6 &0.7 &0.5 \end{bmatrix}$$
(10 marks)
6 (a) Explain Back prorogation algorithm.(10 marks) 6 (b) If a fuzzy set defined by:
$$A=\frac{0.5}{x_{1}}+\frac{0.4}{x_{2}}+\frac{0.7}{x_{3}}+\frac{1}{x_{4}} $$List all α cuts of set A
(10 marks)

Write short note on (any four)

7 (a) Boltzman machine(5 marks) 7 (b) LMS algorithm(5 marks) 7 (c) Brain state in box model(5 marks) 7 (d) Crossover and mutation(5 marks) 7 (e) Bias and threshold in context of artificial neural network(5 marks) 7 (f) Method steepest descent(5 marks) 7 (g) Fuzzy controller(5 marks)


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