JNTU
JNTU B.Tech III ARTIFICIAL NEURAL NETWORKS Previous Question Paper (May - 2019 - R16)
Part A is compulsory. Answer all questions in Part A. Part B consists of 5 Units. Answer any one full question from each unit.
Part A
What is a Neural Network?
Answer
A neural network is a computational model inspired by the structure and functional aspects of biological neural networks. It consists of interconnected processing units called neurons that work together to solve complex problems by learning patterns from input data.
Define Knowledge Representation technique.
Answer
Knowledge representation is the method of encoding information about the world into a format that a computer system can use to perform complex tasks, such as reasoning, inference, or decision-making. It involves mapping real-world entities and relationships into symbolic or numerical structures.
What is Linear Least Square Filters?
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What do you mean by Adaptive Filtering Problem?
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Define Back Propagation method.
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What is Supervised Learning?
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State SOM algorithm.
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What are properties of Feature Map?
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What is Computer Experiment?
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Define Dynamical Systems
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Part B
What are the various active building blocks of neural networks? Explain the current mirror and inverter based neuron in detail.
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Explain about Memory Based Learning and Hebbian Learning.
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Describe about Neural networks viewed as Directed Graphs.
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Write in detailed note on Unconstrained Organization Techniques and Learning curves.
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Explain in detail Perceptron - Convergence theorem.
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Discuss about solution for XOR problem using Back Propagation Algorithm.
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Define Heuristics? Explain about Output Representation and decision Rule.
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Explain about Cross Validation and What is the purpose of Cross Validation.
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Explain about Hessian Matrix in detail.
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What are Network Pruning Techniques? Explain them in detail.
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List and explain Virtues and Limitations of Back Propagation Learning.
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Explain Self Organization Map.
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List and explain any two basic Feature Mapping Models.
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Discuss Computer Simulations and Learning Vector Quantization.
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Describe about Adaptive Pattern Classification.
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Discuss about Stability of Equilibrium States and Attractors.
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List and explain Neuro Dynamical models.
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Write about Hopfield Models in detail.
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Explain about Manipulation of Attractors as a Recurrent Network Paradigm.
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