Math 2.0

Resources / course

Soft Computing Techniques 5HR1Y1BRCF

URIm2://res/5HR1Y1BRCF
Kindcourse
CreatorsSantimoy Kundu (instructor)
Date2025-01-16
Scopein

Expressions

e1: edition

Languageen
Date2025-01-16
Contents
  • part 1: week-01
  • session 1: Lec 01 Fuzzy Sets Fuzzy Membership Function Support Core Crossover points & Bandwidth of a Fuzzy set
  • session 2: Lec 02 Fuzzy Sets and Membership Function Alpha-Cut of a Fuzzy Set Fuzzy subset & Scalar cardinality
  • session 3: Lec 03 Fuzzy Sets & Membership funt. Fuzzy subset & Scalar cardinality Basic operations & Properties
  • session 4: Lec 04 Fuzzy Sets and Membership functions ........ intersection etc. "
  • session 5: Lec 05 Fuzzy Sets Membership functions Basic operations
  • part 2: week-02
  • session 6: Lecture 06: Crisp Relation and Example Fuzzy Relation and operations
  • session 7: Lec 07: Crisp relation and fuzzy relation Extension principle Fuzzy arithmetic numbers & operations
  • session 8: Lec 08 Fuzzy relation and operations Fuzzy multi-valued logic Fuzzy implications
  • session 9: Lec 09: Fuzzy relation and fuzzy implications Fuzzy multi-valued logic Defuzzification
  • session 10: Lec 10: Defuzzification
  • part 3: week-03
  • session 11: Lecture 11 : Fuzzy rules Generalised Modus Ponens Generalised Modus Tollens
  • session 12: Lecture 12 : Fuzzy rules Fuzzy inference system
  • session 13: Lecture 13 : Fuzzy inference system Mamdanis, Sugenos and Tsukamoto type inferences
  • session 14: Lecture 14: Fuzzy inference systems with examples
  • session 15: Lecture 15 : Numerical examples
  • part 4: week-04
  • session 16: Lec 16 Introduction of Genetic Algorithm Darwin’s Principle
  • session 17: Lec 17 Working principle and flow chart of Genetic Algorithm Important terms of Genetic Algorithm
  • session 18: Lec 18 Working procedure of Genetic Algorithm Initial population of Genetic Algorithm
  • session 19: Lec19: Basic Concepts of Genetic Algorithm Some encoding technique of Genetic Algorithm
  • session 20: Lec 20 Working principle of Genetic Algorithm Population Set Encoding Techniques
  • part 5: week-05
  • session 21: Lec 21 Selection operator Population diversity and Selection Pressure Exploration and Exploitation
  • session 22: Lec 22 Population diversity and Selection Pressure Exploration and Exploitation Selection Methods
  • session 23: Lec 23 Selection Methods and Crossover Techniques
  • session 24: Lec 24 Selection Methods Crossover techniques Crossover Probability and Mutation Probability.
  • session 25: Lecture 25: Crossover techniques
  • part 6: week-06
  • session 26: Lecture 26: Numerical Examples by using Genetic Algorithm
  • session 27: Lecture 27: Numerical Examples
  • session 28: Lecture 28: Numerical Examples (contd.)
  • session 29: Lecture 29: Numerical Examples (contd.)
  • session 30: Lecture 30: Numerical Examples (contd.)
  • part 7: week-07
  • session 31: Lec 31 Introduction of Multi-objective optimization
  • session 32: Lec 32 Weighted Sum Method Epsilon-Constraint Method
  • session 33: Lec 33 Weighted Distance Metric Method Lexicographic Ordering Method Reference point method
  • session 34: Lec 34: Few Methods to solve problems of Multi-objective optimization
  • session 35: Lec 35 Introduction of Multi objective GAsVEGA Method"
  • part 8: week-08
  • session 36: Lec 36 Vector Optimized Evolution Strategy (VOES)Weight-Based Genetic Algorithm (WBGA)
  • session 37: Lec 37 NSGA and NSGA-II
  • session 38: Lec 38 Numerical examples by using NSGA-II
  • session 39: Lec 39 Concepts on non-domination Ranking Crowding distance operator NSGA-II
  • session 40: Lec 40 Introduction of SPEA and SPEA 2
  • part 9: week-09
  • session 41: Lec 41: Introduction Rough Set theory
  • session 42: Lec 42 Introduction Rough Set theory Indiscernibility Relation
  • session 43: Lec 43 Indiscernibility Relation Lower Approximations Upper Approximations
  • session 44: Lec 44 Lower Approximations Upper Approximations Boundary Regions
  • session 45: Lec 45 Properties of Approximations and Basic classes of Rough Set theory with Numerical examples
  • part 10: week-10
  • session 46: Lec 46 Rough membership function Dependency of Attributes
  • session 47: Lec 47 Dependency of Attributes Reduct and Core
  • session 48: Lec 48 Indispensable and Dispensable Attributes Reduct and Core
  • session 49: Lec 49 Reduct and Core Discernibility Matrix and Function
  • session 50: Lecture 50: Numerical examples of Discernibility Matrix and Function
  • part 11: week-11
  • session 51: Lec 51 Some concepts on Rough set theory Introduction to Rule Generation
  • session 52: Lec 52: Rule Generation based on Reduct and Core
  • session 53: Lec 53 Some Problems of Rule Generations.
  • session 54: Lec 54 Fuzzy-Rough Sets
  • session 55: Lec 55 Some problems of Fuzzy-Rough sets
  • part 12: week-12
  • session 56: Lec 56 Introduction to Artificial Neural Networks
  • session 57: Lec 57 Numerical example of Backpropagation Algorithm
  • session 58: Lec 58: Intelligent Systems Introduction to Neuro-Fuzzy system
  • session 59: Lecture 59: Adaptive Neuro-Fuzzy Systems
  • session 60: Lecture 60: Ant colony optimization techniques
  • part 13: Live Session
  • session 61: Live Session 20-03-2025
  • session 62: Live Session 17-04-2025

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