Soft Computing And Optimization Algorithms (Hindi)

   Wednesday, October 14, 2020   

Starting a new Lecture Notes Series on Soft Computing And Optimization Algorithms

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Lecture Description of this above Topic:

Lecture 1: Basics Of Computing

Lecture 2: Hard Computing

Lecture 3: Basics Of Soft Computing

Lecture 4: Soft Computing Tools / Paradigm : Fuzzy Logic, Neural Network, Evolutionary Computing Explained

Lecture 5: Hybrid Computing

Lecture 6: Hard Computing Vs Soft Computing

Lecture 7: Basics Of Fuzzy Logic

Lecture 8: Crisp Logic Vs Fuzzy Logic

Lecture 9: Crisp Set

Lecture 10: Fuzzy Set And Membership Function

Lecture 11: Features Of Membership Function

Lecture 12: Discrete And Continuous Membership Function

Lecture 13: Fuzzy Set Operation : Union And Intersection

Lecture 14: Fuzzy Set Operation : Complement

Lecture 15: Fuzzy Set Operations : Vector Product,Scalar Product, Power And Equality Explained with Examples

Lecture 16: Fuzzy Set Operations : Sum,Difference,Disjunctive Sum Explained

Lecture 17: Fuzzy Set Operation : Cartesian Product Explained with Example

Lecture 18: Fuzzy Set Properties

Lecture 19: Crisp Relation

Lecture 20: Operation On Crisp Relation : Union,Intersection,Complement Explained with Examples

Lecture 21: Fuzzy Relation And It's Operations : Union,Intersection,Complement Explained

Lecture 22: Fuzzy IF-THEN Rule / Fuzzy Implication

Lecture 23: Fuzzy IF-THEN Rule Explained with Example

Lecture 24: Fuzzification and Defuzzification

Lecture 25: Lambda-Cut Method ll Defuzzification Methods

Lecture 26: Lambda-Cut Method On Fuzzy Relation

Lecture 27: Maxima Methods ll Defuzzification Technique

Lecture 28: Weighted Average Method

Lecture 29: Centroid Methods : Center Of Sum (COS)

Lecture 30: Centroid Methods : Center Of Gravity Method

Lecture 31: Truth Value and Tables in Fuzzy Logic

Lecture 32: Fuzzy Proposition and Crisp Proposition

Lecture 33: Decomposition Of Rules

Lecture 34: Aggregation Of Fuzzy Rules

Lecture 35: Introduction To Genetic Algorithm Explained

Lecture 36: Genetic Algorithm Explained With Flowchart

Lecture 37: Simple Genetic Algorithm (SGA) Explained With Flowchart

Lecture 38: Genetic Algorithm Vs Traditional Algorithm Explained

Lecture 39: Convergence Criteria / Termination Condition in Genetic Algorithm Explained

Lecture 40: Encoding Technique : Binary Encoding in Genetic Algorithm Explained with Examples

Lecture 41: Fitness Evaluation : Fitness Function / Objective Function Explained with Examples

Lecture 42: Single Point Crossover, Two Point Crossover, MultiPoint Crossover Explained with Examples

Lecture 43: Uniform Crossover with Crossover Mask Explained with Solved Example

Lecture 44: Mutation in Genetic Algorithm

Lecture 45: Tournament Selection in Genetic Algorithm Explained

Lecture 46: Roulette Wheel Selection in Genetic Algorithm Explained with Example

Lecture 47: Rank Based Selection in Genetic Algorithm Explained With Example

Lecture 48: Particle Swarm Optimization (PSO) Algorithm Part-1

Lecture 49: Particle Swarm Optimization (PSO) Algorithm Part-2 Explained