
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.
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
Student Ratings & Reviews
No Review Yet