Fuzzy Logic and Genetic Programming

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FUZZY LOGIC AND BE05000461
FREE PDF BE05000461
Code: BE05000461 Credits: 2 Semester 5 Electrical Engineering

Fuzzy Logic and Genetic Programming

Get the full GTU study material, English & Gujarati textbook PDFs, syllabus curriculum, and solved previous year question papers.

Fuzzy Logic and Genetic Programming Syllabus — Unit-wise Content

The official GTU curriculum for Fuzzy Logic and Genetic Programming (subject code BE05000461) as prescribed for Electrical Engineering · Semester 5 divides the course into 6 units, covering 25 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.

Download the official syllabus PDF (BE05000461)

Unit 1 Introduction to Soft Computing

  • Soft computing vs hard computing
  • Characteristics and applications
  • Components: Fuzzy Logic
  • Genetic Algorithms
  • Real-world applications 4 Program Name: Bachelor of Engineering Level: UG Branch: All (Except Computer Engineering & Allied Branches) Subject Code: BE05000461 Subject Name: Fuzzy Logic and Genetic Programming w.e.f.2024-25 http://syllabus.gtu.ac.in/ Pageno.2of2

Unit 2 Fuzzy Logic Fundamentals

8 lecture hours

  • Crisp sets vs fuzzy sets
  • Membership functions (triangular
  • Operations on fuzzy sets
  • Fuzzy relations and properties

Unit 3 Fuzzy Inference Systems

10 lecture hours

  • Fuzzification methods
  • defuzzification methods
  • Fuzzy rule-based systems
  • Mamdani and Sugeno models
  • Fuzzy controller design

Unit 4 Genetic Algorithms - Concepts

6 lecture hours

  • basic concepts
  • Basic structure of GA, encoding
  • fitness function

Unit 5 Genetic Algorithms - Operations

12 lecture hours

  • Genetic modeling: inheritance operators
  • cross over
  • inversion and deletion
  • mutation operator
  • bitwise operator
  • generational cycle
  • Convergence of GA, application and advance in GA, difference and similarity between GA, and other traditional methods

Unit 6 Advanced GA & Hybrid System

5 lecture hours

  • Optimization problems using GA, Fuzzy-Genetic algorithm

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