SOFT COMPUTING TECHNIQUES

Paper Code: 
MCA 325C
Credits: 
04
Periods/week: 
04
Max. Marks: 
100.00
Objective: 

This course enables the students to

  1. Apply the mathematical concepts for carrying out the optimization associated with different soft computing techniques
  2. Analyze current research problems and research methods in Soft Computing
  3. Evaluate different soft computing techniques
  4. create and extend the basic skills necessary to pursue research in Soft Computing

 

Course Outcomes(COs):

Learning Outcome (at course level)

Learning and teaching strategies

Assessment Strategies

CLO129. Apply knowledge of Artificial Neural Networks to real life problems

CLO130. Analyze genetic algorithms and its applicability in research oriented problems

CLO131. Evaluate      the       concept   and applicability of hybrid systems

CLO132. Create skills necessary to follow research in soft computing

Approach in teaching:

Interactive Lectures, Modeling, Discussions, implementing enquiry based learning, student centered approach, Through audio-visual aids

Learning activities for the students:

Experiential Learning, Presentations, Discussions, Quizzes and Assignments

 

  • Assignments
  • Written test in classroom
  • Classroom Activity
  • Continuous Assessment
  • Semester End Examination

 

 

 

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, Introduction to Deep Learning

 

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: 
  • S. Rajasekaran and G.A. Vijaylakshmi Pai, “Neural Networks Fuzzy Logic, and Genetic Algorithms”, Prentice Hall of India 2004.
  • K.H. Lee. First Course on Fuzzy Theory and Applications, Springer-Verlag, 2005

 

Academic Year: 
2021-22
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