B.E. Computer Science & Engineering:Neural Networks and Fuzzy Logic

Thapar University
In Patiala

Price on request
You can also call the Study Centre
17523... More
Compare this course with other similar courses
See all

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
See map

Course programme

First Year: Semester I

Mathematics I
Engineering graphics
Computer Programming
Physics
Solid Mechanics
Communication Skill

First Year: Semester-II

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

Second Year- Semester - I

Measurement Science and Techniques
Optimization Techniques
Semiconductor Devices
Data Structures
Discrete Mathematical Structures
Digital Electronic Circuits
Human Values, Ethics and IPR

Second Year- Semester – II

Numerical and Statistical Methods
Electrical Engineering Materials
Computer System Architecture
Principles of Programming Languages
Analysis and Design of Information Systems
Operating Systems
Environmental Studies


Third Year- Semester – I

Object Oriented Programming
Theory of Computation
Computer Networks
Data Base Management Systems
Software Engineering
Microprocessors
Summer Training

Third Year- Semester – II

Total Quality Management
Algorithm Analysis and Design
Software Project Management
Internet and Web Technologies

Fourth Year- Semester – I

Engineering Economics
System Software
Compiler Construction
Computer Graphics
Artificial Intelligence

Fourth Year- Semester – II

Project Semester
Project
Industrial Training(6 weeks)


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.


Compare this course with other similar courses
See all