ARTIFICIAL NEURAL NETWORKS (PE – III) B.Tech. IV Year I Sem JNTUH R-18

• How is the biological neural network similar to an Artificial Neural Network (ANN)?

• Explain different models of a neuron used in ANNs.

• Draw and explain various network architectures for ANNs.

• How can Knowledge Representation be achieved using ANNs?

• Compare and contrast the roles of Artificial Intelligence and Neural Networks.

• What are the different types of learning processes in ANNs?

• Explain the challenges of the credit assignment problem in neural networks.

Unit II: Single Layer Perceptrons

• Can you solve the Adaptive Filtering Problem using a single layer perceptron?

• What are the different unconstrained organization techniques for single layer perceptrons?

• Derive the Least Mean Square (LMS) algorithm for training single layer perceptrons.

• Explain the significance of learning curves and learning rate annealing in perceptron training.

• Prove the convergence theorem for single layer perceptrons.

• Show how a single layer perceptron can act as a Bayes classifier for a Gaussian environment.

Unit III: Multilayer Perceptron and Backpropagation

• Explain the Backpropagation Algorithm for training multilayer perceptrons.

• Discuss the role of the Hessian matrix in backpropagation.

• How can cross-validation be used to determine the generalization ability of a neural network?

• Describe different network pruning techniques used to improve performance.

• What are the advantages and limitations of backpropagation learning?

• Explain algorithms for accelerated convergence in backpropagation.

Unit IV: Self-Organization Maps (SOM)

• Compare and contrast Kohonen's Self-Organizing Map (SOM) with other feature mapping models.

• Describe the SOM algorithm and explain its properties.

• Conduct computer simulations to visualize the formation of feature maps using SOM.

• Explain the benefits of Learning Vector Quantization (LVQ) for adaptive pattern classification.

Unit V: Neuro Dynamics and Hopfield Models

• Define dynamical systems and explain the concept of stability of equilibrium states.

• Analyze the behavior of attractors in neurodynamical models.

• How can attractors be manipulated in recurrent neural networks?

• Describe the structure and functionality of Hopfield models.

• Explain the relationship between Hopfield models and restricted Boltzmann machines.