Soft Computing (IT-701) - Important Questions

Describe the McCulloch-Pitts neuron model, its working, learning ability, and how it implements simple logic functions.

Explain the structure and functioning of a "biological neural network" and compare it with an "Artificial Neural Network (ANN)".

Define "Linear Separability" and explain why the "XOR problem" cannot be solved using a single-layer perceptron?

State the difference between supervised learning and unsupervised learning.

Explain the structure of a neuron and its various components. Also discuss the Hebb Rule in the context of neural networks.

Define neural network and explain the biological neural network / flow of information in a neural cell with a neat sketch.

Write short notes on evolution of neural networks.

Explain the backpropagation algorithm and its phases for training multilayer neural networks

Compare/explain Adaline and Madaline networks with respect to architecture and learning.

Explain the architecture of a single-layer feed-forward neural network / perceptron model with diagram and activation function.

Explain the architecture (with sketch) and training/testing algorithm of ADALINE.

Explain the perceptron learning rule and training algorithm for a single-output perceptron, including its convergence characteristics.

Discuss the architecture and training algorithm of multilayer feed-forward network

What is classification? Discuss a classification algorithm.

What is the RBFN method? Also discuss the back propagation network.

What is the Softmax function and why is it needed?

Discuss the application of neural network in forecasting and data compression.

Explain the Kohonen Self-Organizing Map (SOM) network, its architecture and working processes/algorithm with neat diagram and flowchart.

Explain the architecture and algorithm of the (forward-only) counterpropagation network with diagram.

Differentiate between Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) with application examples.

What is a Convolutional Neural Network (CNN) and how does it work?

Distinguish between recurrent and non-recurrent networks.

Write short notes on Adaptive Resonance Theory 1.

Explain the properties and set-theoretic operations of fuzzy sets with examples.

List and explain applications of fuzzy logic in engineering problems.

What is fuzzy composition? Discuss its types.

Explain the various methods of defuzzification.

Construct a Fuzzy Inference System (FIS) for temperature control using appropriate fuzzy rules.

Explain "fuzzy if-then rules" with an example. How does fuzzy reasoning differ from conventional logic reasoning?

Given fuzzy sets $A_1 = 0.2/x_1 + 0.9/x_2$ and $A_2 = 0.3/y_1 + 0.5/y_2 + 1/y_3$, determine $A_1 - A_2$ or $A_1 + A_2$.

Write short notes on :

Discuss in brief the Fuzzy rules and reasoning.

Explain in detail the block diagram of a Fuzzy Inference System.

Write a short note on any two.

Explain different encoding schemes used in genetic algorithm.

Explain genetic algorithm with flowchart and discuss selection, crossover, and mutation operators.

Describe the Schema Theorem and explain how it supports the working of genetic algorithms

Discuss the Ant Colony Optimization method.

State any two features of Genetic algorithm. Also discuss Parallel Genetic algorithm.

Explain the role of fitness function and requirements of genetic algorithm.

Discuss about the solution provided to travelling sales man problem using genetic algorithm.

Write short notes on Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).

Derive Bayes rule. Discuss fuzzy Bayesian decision making.

Discuss the application areas of Neuro-fuzzy hybrid systems.

What are the advantages of ART network? Discuss about the Gain control in ART network.

Classify the various types of soft computing techniques and mention some application areas for NN.

Write short notes on any two of the following: