Soft Computing Techniques

Paper Code: 
MCA 524E
Credits: 
04
Periods/week: 
04
Max. Marks: 
100.00
Objective: 
  • To understand the basic concept, approaches and techniques in ML
  • To develop a deeper understanding of fuzzy logic and neural networks.
  • To develop the basic skills necessary to pursue research in SC
12.00
Unit I: 
Introduction to Soft Computing

Introduction of Hard and Soft Computing, Unique features of Soft computing, Components of Soft computing, Fuzzy Computing, Evolutionary Computation, Genetic Algorithm, Swarm Intelligence, Ant Colony Optimizations, Neural Network, Machine Learning , Associative Memory, Adaptive Resonance Theory

14.00
Unit II: 
Fuzzy Logic

Basic concepts of fuzzy logic, Fuzzy sets and Crisp sets, Fuzzy set theory and operations, Properties of fuzzy sets, Fuzzy and Crisp relations, Fuzzy to Crisp conversion, Membership functions, interference in fuzzy logic, fuzzy if-then rules, Fuzzy implications and Fuzzy algorithms, Fuzzyfications&Defuzzificataions, Fuzzy Inference Systems, Mamdani Fuzzy Model, Sugeno Fuzzy Model, Fuzzy Controller, applications.

14.00
Unit III: 
Neural Networks

Introduction and Architecture: Neuron, Nerve structure and synapse, Artificial Neuron and its model, Neural network architecture: single layer and multilayer feed forward networks, recurrent networks. Back propagation networks architecture: perceptron model, solution, single layer artificial neural network, multilayer perception model; back propagation learning methods, back propagation algorithm, applications

10.00
Unit IV: 
Genetic Algorithms

Basic concepts of GA, working principle, procedures of GA, flow chart of GA, Genetic representations, (encoding) Initialization and selection, Genetic operators, Mutation, Generational Cycle, applications.

10.00
Unit V: 
Hybrid Systems

Integration of neural networks, fuzzy logic and genetic algorithms. GA Based Back Propagation Networks, Fuzzy Back Propagation Networks, Fuzzy Associative Memories, Simplified Fuzzy ARTMAP

ESSENTIAL READINGS: 
  1. S. Rajasekaran and G.A.VijaylakshmiPai.. Neural Networks Fuzzy Logic, and Genetic Algorithms, Prentice Hall of India 2004.
  2. K.H.Lee.. First Course on Fuzzy Theory and Applications, Springer-Verlag.
REFERENCES: 
  1. J. Yen and R. Langari.. Fuzzy Logic, Intelligence, Control and Information, Pearson Education.
  2. N.P.Padhy,”Artificial Intelligence and Intelligent Systems” Oxford University Press. Reference Books
Academic Year: 
2018-19
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