Soft Computing

Learning Outcomes: 
After learning the course the students should be able to:
Identify and describe soft computing techniques and their roles in building intelligent machines
Recognize the feasibility of applying a soft computing methodology for a particular problem
Apply fuzzy logic and reasoning to handle uncertainty and solve engineering problems
Apply genetic algorithms to combinatorial optimization problems
Apply neural networks to pattern classification and regression problems
Effectively use existing software tools to solve real problems using a soft computing approach
Evaluate and compare solutions by various soft computing approaches for a given problem.
Syllabus: 
Unit NoTopics
1
Inroduction
What is soft computing? Differences between soft computing and hard computing, Soft Computing constittuents, Methods in soft computing, Applications of Soft Computing
2
Introduction to Genetic Algorithms
Introduction to Genetic Algorithms (GA), Representation, Operators in GA, Fitness function, population, building block hypothesis and schema theorem.; Genetic algorithms operators- methods of selection, crossover and mutation, simple GA(SGA), other types of GA, generation gap, steady state GA, Applications of GA
3
Neural Networks
Concept, biological neural syste,. Evolution of neural network, McCulloch-Pitts neuron model, activation functions, feedforward networks, feedback networks, learning rules – Hebbian, Delta, Percepron learning and Windrow-Hoff, winner-take-all
4
Supercised Learning
Perceptron learning, single l layer/multilayer perceptron, linear separability, hidden layers, back popagation algorithm, Radial Basis Function network; Unsupervised learning - Kohonen, SOM, Counter-propagation, ART, Reinforcement learning, adaptive resonance architecture, applications of neural networks to pattern recognition systems such as character recognition, face recognition, application of neural networks in image processing
5
Fuzzy System
Basic definition and terminology, set-theoretic operations, Fuzzy Sets, Operations on Fuzzy Sets, Fuzzy Relations, Membership Functions, Fuzzy Rules & Fuzzy Reasoning, Fuzzy Inference Systems, Fuzzy Expert Systems, Fuzzy Decision Making; Neuro-fuzzy modeling- Adaptive Neuro-Fuzzy Inference Systems, Coactive Neuro-Fuzzy Modeling, Classification and Regression Trees, Data Clustering Algorithms, Rulebase Structure Identification and Neuro-Fuzzy Control , Applications of neuro-fuzzy modeling
6

Swarm Intelligence

What is swarm intelligence? Various animal behavior which have been used as examples, ant colony optimization, swarm intelligence in bees, flocks of birds, shoals of fish, ant-based routing, particle swarm optimization

Text Books: 
Name : 
Principle of soft computing
Author: 
S.N. Shivanandam
Publication: 
Wiley
Name : 
Neuro-Fuzzy and Soft Computing
Author: 
Jyh-Shing Roger Jang
Chuen-Tsai Sun
EijiMizutani
Publication: 
Prentice-Hall of India, 2003.
Name : 
Fuzzy Sets and Fuzzy Logic-Theory and Applications
Author: 
George J. Klir and Bo Yuan
Publication: 
Prentice Hall, 1995
Name : 
Neural Networks Algorithms, Applications, and Programming Techniques
Author: 
James A. Freeman and David M. Skapura
Publication: 
Pearson Edn., 2003
Reference Books: 
Name: 
An Introduction to Genetic Algorithm
Author: 
Mitchell Melanie
Publication: 
Prentice Hall
Edition: 
1998
Name: 
Genetic Algorithms in Search, Optimization & Machine Learning
Author: 
David E. Goldberg
Publication: 
Addison Wesley
Edition: 
1997
Syllabus PDF: 
AttachmentSize
PDF icon Sem 5 BDA-Soft Computing.pdf191.5 KB
branch: 
BDA
Course: 
2018
Stream: 
B.Tech