IF7301 SOFT COMPUTING - ANNA UNIV PG 1ST SEM SYLLABUS - Anna University Internal marks 2018

IF7301 SOFT COMPUTING - ANNA UNIV PG 1ST SEM SYLLABUS

ANNA UNIVERSITY, CHENNAI
REGULATIONS - 2013
M.E. APPLIED ELECTRONICS
IF7301 SOFT COMPUTING

COURSE OBJECTIVES:
 To learn the key aspects of Soft computing and Neural networks.
 To know about the components and building block hypothesis of Genetic algorithm.
 To understand the features of neural network and its applications
 To study the fuzzy logic components
 To gain insight onto Neuro Fuzzy modeling and control.
 To gain knowledge in machine learning through Support vector machines.

UNIT I INTRODUCTION TO SOFT COMPUTING
Evolution of Computing - Soft Computing Constituents – From Conventional AI to
Computational Intelligence - Machine Learning Basics

UNIT II GENETIC ALGORITHMS
Introduction, Building block hypothesis, working principle, Basic operators and Terminologies
like individual, gene, encoding, fitness function and reproduction, Genetic modeling:
Significance of Genetic operators, Inheritance operator, cross over, inversion & deletion,
mutation operator, Bitwise operator, GA optimization problems, JSPP (Job Shop Scheduling
Problem), TSP (Travelling Salesman Problem),Differences & similarities between GA & other
traditional methods, Applications of GA.

UNIT III NEURAL NETWORKS
Machine Learning using Neural Network, Adaptive Networks – Feed Forward Networks
– Supervised Learning Neural Networks – Radial Basis Function Networks - Reinforcement
Learning – Unsupervised Learning Neural Networks – Adaptive Resonance Architectures –
Advances in Neural Networks.

UNIT IV FUZZY LOGIC
Fuzzy Sets – Operations on Fuzzy Sets – Fuzzy Relations – Membership Functions-Fuzzy
Rules and Fuzzy Reasoning – Fuzzy Inference Systems – Fuzzy Expert Systems – Fuzzy
Decision Making

UNIT V NEURO-FUZZY MODELING
Adaptive Neuro-Fuzzy Inference Systems – Coactive Neuro-Fuzzy Modeling – Classification
and Regression Trees – Data Clustering Algorithms – Rule base Structure Identification –
Neuro-Fuzzy Control – Case Studies.

TOTAL : 45 PERIODS

COURSE OUTCOMES
 Implement machine learning through Neural networks.
 Develop a Fuzzy expert system.
 Model Neuro Fuzzy system for clustering and classification.
 Write Genetic Algorithm to solve the optimization problem
 Use Support Vector Machine for enabling the machine learning

REFERENCES:
1. Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani, “Neuro-Fuzzy and Soft Computing”,
Prentice-Hall of India, 2003.
2. Kwang H.Lee, “First course on Fuzzy Theory and Applications”, Springer–Verlag Berlin
Heidelberg, 2005.
3. george j. klir and bo yuan, “fuzzy sets and fuzzy logic-theory and applications”, prentice hall,
1995.
4. james a. freeman and david m. skapura, “neural networks algorithms, applications, and
programming techniques”, pearson edn., 2003.
5. david e. goldberg, “genetic algorithms in search, optimization and machine learning”,
addison wesley, 2007.
6. mitsuo gen and runwei cheng,”genetic algorithms and engineering optimization”, wiley
publishers 2000.
7. mitchell melanie, “an introduction to genetic algorithm”, prentice hall, 1998.
8. s.n.sivanandam, s.n.deepa, “introduction to genetic algorithms”, springer, 2007.
9. eiben and smith “introduction to evolutionary computing” springer
10. e. sanchez, t. shibata, and l. a. zadeh, eds., "genetic algorithms and fuzzy logic systems:
soft computing perspectives, advances in fuzzy systems - applications and theory", vol. 7,
river edge, world scientific, 1997.

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