B.E. Electronics & Comm. Engg:Neural Networks and Fuzzy Logic

Thapar University
In Patiala

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Important information

  • Bachelor
  • Patiala
  • Duration:
    4 Years
Description

Important information
Venues

Where and when

Starts Location
On request
Patiala
Thapar University P.O Box 32, 147004, Punjab, India
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Course programme

First Year: Semester I

Mathematics I
Engineering graphics
Computer Programming
Physics
Solid Mechanics
Communication Skills

First year: Semester II

Mathematics II
Manufacturing Process
Chemistry
Electrical and Electronic Science
Thermodynamics
Organizational Behavior


Second year: Semester I

Numerical and Statistical Methods
Measurement Science and Techniques
Electromagnetic Fields
Semiconductor Devices
Signals and Systems
Digital Electronic Circuits
Human Values, Ethics and IPR


Second year: Semester II

Optimization Techniques
Analog Electronic Circuits
Networks and Transmission Lines
Electrical Engineering Materials
Analog Communication Systems
Data Structure and Information Technology
Environmental Studies


Third year: Semester I

Digital Signal Processing for Communications
Microprocessors
VLSI Circuit Design
Digital Communication Systems
Microelectronics Technology
Linear Integrated Circuits and Applications
Summer Training(6 weeks)

Third year: Semester II

Project Semester
Project
Industrial Training(6 weeks)

Fourth year: Semester I

Antenna and Wave Propagation
Modern Control Engineering
Wireless and Mobile Communication Systems
Microwave Engineering
Engineering Economics


Fourth year: Semester II

Optical Communication Systems
Advanced Communication Systems
HDL Based Digital Design
Total Quality Management
Minor Project

Neural Networks and Fuzzy Logic

Introduction: History of neural networks, structure and function of a single neuron, biological neurons, artificial neuron models, Types of activation functions. Neural Network Architectures: Fully connected, layered, cyclic feed forward.

Neural learning: correlation, competitive, feedback based weight adaptation, evaluation of networks; quality of results, generalizability, computational resources,

Supervised learning: perceptrons, linear separability, Multilayer networks, backpropagation algorithm and its variantions, Unsupervised learning, winner-take all networks, adaptive resonance theory, Self organizing maps, Hopfield networks, Boltzman machines, application in identification, optimization, pattern recognition etc.

Broad application areas of neural networks: classification, clustering, pattern association, function approximation, forecasting,

Fuzzy Logic: Fuzziness vs probability, Crisp logic vs fuzzy logic, Fuzzy sets and systems, operation on sets, fuzzy relations, membership functions, fuzzy rule generation, de fuzzy controllers, Fuzzy applications in consumer products. Operation research, controls.

Genetic algorithms: Introduction and concept, coding reproduction, cross-over and mutation. Scaling, fitness, applications.

Hybrid Techniques: Neuro-fuzzy systems, Fuzzy expert system.


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