Skip to content
CampusPrep
IT-701 · Soft Computing/Official Syllabus

Soft Computing (IT-701) - Official Syllabus

1
Unit 1

Introduction to Neural Network

  • Concept
  • biological neural network
  • comparison of ANN with biological NN
  • evolution of artificial neural network
  • Basic models
  • Types of learning
  • Linear separability
  • XOR problem
  • McCulloch-Pitts neuron model
  • Hebb rule
2
Unit 2

Supervised Learning

  • Perceptron learning
  • Single layer/multilayer
  • Adaline
  • Madaline
  • Back propagation network
  • RBFN
  • Application of Neural network in forecasting
  • data compression and image compression
3
Unit 3

Unsupervised learning

  • Introduction
  • Fixed weight competitive nets
  • Kohonen SOM
  • Counter Propagation networks
  • Introduction to Convolutional neural networks (CNN)
  • Recurrent neural networks (RNN)
4
Unit 4

Fuzzy Set

  • Introduction
  • Basic Definition and Terminology
  • Properties and Set-theoretic Operations
  • Fuzzy Relations
  • Membership Functions and their assignment
  • Fuzzy rules and fuzzy Reasoning
  • Fuzzy if-then Rules
  • Fuzzy Inference Systems
  • Application of Fuzzy logic in solving engineering problems
5
Unit 5

Genetic Algorithm

  • Introduction to GA
  • Simple Genetic Algorithm
  • terminology and operators of GA (individual, gene, fitness, population, data structure, encoding, selection, crossover, mutation, convergence criteria)
  • Reasons for working of GA and Schema theorem
  • GA optimization problems like TSP (Travelling salesman problem)
  • Network design routing
  • Introduction to Ant Colony optimization (ACO)
  • Particle swarm optimization (PSO)

Laboratory Experiments

  • Form a perceptron net for basic logic gates with binary input and output
  • Using Adaline net, generate XOR function with bipolar inputs and targets
  • Calculation of new weights for a Back propagation network, given the values of input pattern, output pattern, target output, learning rate and activation function
  • Design fuzzy inference system for a given problem
  • Maximize the function y =3x2 + 2 for some given values of x using Genetic algorithm
  • Implement Travelling salesman problem using Genetic Algorithm
  • Optimisation of problem like Job shop scheduling using Genetic algorithm

Reference Books

  • Principle of soft computing
  • Neural Network, Fuzzy logic And Genetic Algorithm
  • Introduction to Artificial Neural Network System
  • Neural Network- A Comprehensive Foundation
  • Fuzzy logic with Engineering Applications
Go to where you left off?

Quick Add to Notes

Save questions, your own notes and screenshots into notes filed by unit. It takes a free account.

Create free account

Have an account? Log in