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