Introduction To Soft Computing in 2020

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About Course

Introduction To Soft Computing

Soft computing is an emerging approach to computing that parallels the remarkable ability of the human mind to reason and learn in an environment of uncertainty and imprecision. Soft computing is based on some biological inspired methodologies such as genetics, evolution, ant’s behaviors, particles swarming, human nervous systems, etc. Now, (SC) is the only solution when we don’t have any mathematical modeling of problem-solving (i.e., algorithm), need a solution to a complex problem in real-time, easy to adapt with a changed scenario,s and can be implemented with parallel computing. It has enormous applications in many application areas such as medical diagnosis, computer vision, handwritten character recondition, pattern recognition, machine intelligence, weather forecasting, network optimization, VLSI design, etc.
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What Will You Learn?

  • -Introduction to Soft Computing, Introduction to Fuzzy logic, Fuzzy membership functions, Operations on Fuzzy sets
  • -Fuzzy relations, Fuzzy propositions, Fuzzy implications, Fuzzy inferences
  • -Defuzzification Techniques-I, Defuzzyfication Techniques-II, Fuzzy logic controller-I, Fuzzy logic controller-II
  • -Solving optimization problems, Concept of GA, GA Operators: Encoding, GA Operators: Selection-I
  • -GA Operators: Selection-II, GA Operators: Crossover-I, GA Operators: Crossover-II, GA Operators: Mutation
  • -ANN Training-I, ANN Training-II, ANN Training-III, Applications of ANN
  • -And much more

Course Content

Introduction To Soft Computing

  • Lecture 1 Introduction to soft computing
    00:00
  • Lecture 29 : Pareto-Based approaches to solve MOOPs
    00:00
  • Lecture 28 : Non-Pareto based approaches to solve MOOPs (Contd.)
    00:00
  • Lecture 27 : Non-Pareto based approaches to solve MOOPs
    00:00
  • Lecture 26 : Concept of domination
    00:00
  • Lecture 25 : Multi-objective optimization problem solving (Contd.)
    00:00
  • Lecture 24 : Multi-objective optimization problem solving
    00:00
  • Lecture 23 : GA Operator : Mutation and others
    00:00
  • Lecture 22 : GA Operator : Crossover (Contd.)
    00:00
  • Lecture 30 : Pareto-based approaches to solve MOOPs (contd..)
    00:00
  • Lecture 31 : Pareto-based approach to solve MOOPs
    00:00
  • Lecture 39 : Training ANNs (Contd..)
    00:00
  • Lecture 38 : Training ANNs (Contd..)
    00:00
  • Lecture 37 : Training ANNs (Contd..)
    00:00
  • Lecture 36 : Training ANNs
    00:00
  • Lecture 35 : ANN Architectures
    00:00
  • Lecture 34 : Introduction to Artificial Neural Network
    00:00
  • Lecture 33 : Pareto-based approach to solve MOOPs (contd)
    00:00
  • Lecture 32 : Pareto-based approach to solve MOOPs (contd.)
    00:00
  • Lecture 21 : GA Operator : Crossover (Contd.)
    00:00
  • Lecture 20 : GA Operator: Crossover techniques
    00:00
  • Lecture 9 : Defuzzification techniques (Part-I)
    00:00
  • Lecture 8 : Fuzzy Inferences
    00:00
  • Lecture 7 : Fuzzy implications
    00:00
  • Lecture 6 : Fuzzy Relations (contd.) & Fuzzy propositions
    00:00
  • Lecture 5 : Fuzzy relations
    00:00
  • Lecture 4 : Fuzzy operations
    00:00
  • Lecture 3 : Fuzzy membership functions (Contd.) and Defining Membership functions
    00:00
  • Lecture 2 : Introduction to Fuzzy Logic
    00:00
  • Lecture 10 : Defuzzification Techniques (Part-I) (contd.)
    00:00
  • Lecture 11 : Fuzzy logic controller
    00:00
  • Lecture 19 : GA Operator Selection (Contd.)
    00:00
  • Lecture 18 : GA Operator : Selection
    00:00
  • Lecture 17 : GA operator : encoding scheme (contd.)
    00:00
  • Lecture 16 : GA Operator : Encoding schemes
    00:00
  • Lecture 15 : Concept of Genetic Algorithm (Contd.) and GA Strategies
    00:00
  • Lecture 14 : Concept of Genetic Algorithm
    00:00
  • Lecture 13 : Fuzzy logic controller (Cond.)
    00:00
  • Lecture 12 : Fuzzy Logic Controller (Contd.)
    00:00
  • Lecture 40 : Soft computing tools
    00:00

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