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SYLLABUS | B. TECH. ELECTRONICS & COMMUNICATION ENGINEERING | Neural Networks and Fuzzy

13040708 Neural Networks and Fuzzy L T P C
Version1.1 Date of Approval: Jun 06, 2013 3 1 0 3
Pre-requisites//Exposure Control Systems
co-requisites  

 Course Objectives

After the completion of course the students will

  1. Get the exposure to Artificial Neural Networks & Fuzzy Logic.
  2. Understand the importance of tolerance of imprecision and uncertainty for design of robust & low cost intelligent machines.

Course Outcomes

On completion of this course, the students will be able to

  1. Identify and describe Fuzzy Logic and Artificial Neural Network techniques in building intelligent machines
  2. Apply Artificial Neural Network & Fuzzy Logic models to handle uncertainty and solve engineering problems.
  3. Recognize the feasibility of applying a Neuro-Fuzzy model for a particular problem

Catalog Description

The objective of this course is to present sufficient background in both fuzzy and neural network so that students in future can pursue advanced soft computing methodologies. This course combines knowledge, techniques, and methodologies from various sources, using techniques from neural networks and fuzzy set theory, As an extension, the course uses the Neuro Fuzzy models for the complex engineering problems.

Text Books

  1. Ross, Timothy J. Fuzzy logic with engineering applications. John Wiley & Sons, 2009.
  2. Yegnanarayana, B. Artificial neural networks. PHI Learning Pvt. Ltd., 2004.

Reference Books

  1. Zurada, Jacek M. Introduction to artificial neural systems, West St. Paul, 1992.
  2. Hagan, Martin T., Howard B. Demuth, and Mark H. Beale. Neural network design. Boston: Pws Pub., 1996.
  3. Haykin, Simon. Neural networks: a comprehensive foundation. Prentice Hall PTR, 1994.
  4. Passino, Kevin M., and Stephen Yurkovich. Fuzzy control. Vol. 42. Menlo Park, CA: Addison-Wesley, 1998. 

Course Content

Unit I: Introduction to Artificial Neural Network                       

9 lecture hours

Artificial neural networks and their biological motivation: Terminology, Models of neuron, Topology, characteristics of artificial neural networks, types of activation functions; learning methods: error correction learning, Hebbian learning, Perceptron: XOR Problem, Perception learning rule convergence theorem; Adaline.

Unit II: Feedforward and Recurrent Neural Networks              

9 lecture hours

Architecture: perceptron model, solution, single layer artificial neural network, multilayer

perceptron model; back propogation learning methods, effect of learning rule co-efficient ;back propagation algorithm, factors affecting backpropagation training, applications; Recurrent neural networks: Linear auto associator – Bi-directional associative memory – Hopfield neural network.

Unit III: Fuzzy Logic & Fuzzy Sets                                                            

8 lecture hours

Introduction to Fuzzy Logic, Classical and Fuzzy Sets, Membership Function ,Membership Grade, Universe of Discourse, Linguistic Variables, Operations on Fuzzy Sets: Intersections, Unions, Negation, Product, Difference, Properties of Classical set and Fuzzy sets, Fuzzy vs Probability, Fuzzy Arithmetic, Fuzzy Numbers. 

Unit IV: Fuzzy Relations & Aggregations                                     

8  lecture hours

Essential Elements of Fuzzy Systems, Classical Inference Rule, Classical Implications and Fuzzy Implications, Crisp Relation and Fuzzy Relations, Composition of fuzzy relations, Cylindrical Extension and Projection. Fuzzy IF-THEN rules, Inference: Scaling and Clipping Method, Aggregation, Fuzzy rule based Model: Mamdani Model, TSK model, Fuzzy Propositions, Defuzzification: MOM, COA

Unit V: Fuzzy Optimization and Neuro Fuzzy Systems              

6 lecture hours

Fuzzy optimization –one-dimensional optimization. Introduction of Neuro-Fuzzy Systems, Architecture of Neuro Fuzzy Networks.

Mode of Evaluation: The theory performance of students are evaluated .

  Theory Theory and laboratory
Components Internal SEE
Marks 50 50
Total Marks 100
Scaled Marks 100 100

Relationship between the Course Outcomes (COs) and Program Outcomes (POs)

Mapping between COs and POs
Sl. No. Course Outcomes (COs) Mapped Programme Outcomes
1 Identify and describe Fuzzy Logic and Artificial Neural Network techniques in building intelligent machines

 

1
2 Apply Artificial Neural Network & Fuzzy Logic models to handle uncertainty and solve engineering problems.

 

2
3 Recognize the feasibility of applying a Neuro-Fuzzy model for a particular problem

 

3

 

    Engineering Knowledge Problem analysis Design/development of solutions Conduct investigations of complex problems Modern tool usage The engineer and society Environment and sustainability Ethics Individual or team work Communication Project management and finance Life-long Learning
    1 2 3 4 5 6 7 8 9 10 11 12
TEC461 Neural Networks and Fuzzy Control 2 3 2                  

1=addressed to small extent

2= addressed significantly

3=major part of course

Theory  The theory of this course is used to evaluate the program outcome PO(2)
ADMISSIONS 2021