Lecture 1 Introduction to soft computing.
Lecture 2 : Introduction to Fuzzy Logic.
Lecture 3 : Fuzzy membership functions (Contd.) and Defining Membership functions.
Lecture 4 : Fuzzy operations.
Lecture 5 : Fuzzy relations.
Lecture 6 : Fuzzy Relations (contd.) & Fuzzy propositions.
Lecture 7 : Fuzzy implications.
Lecture 8 : Fuzzy Inferences.
Lecture 9 : Defuzzification techniques (Part-I).
Lecture 10 : Defuzzification Techniques (Part-I) (contd.).
Lecture 11 : Fuzzy logic controller.
Lecture 12 : Fuzzy Logic Controller (Contd.).
Lecture 13 : Fuzzy logic controller (Cond.).
Lecture 14 : Concept of Genetic Algorithm.
Lecture 15 : Concept of Genetic Algorithm (Contd.) and GA Strategies.
Lecture 16 : GA Operator : Encoding schemes.
Lecture 17 : GA operator : encoding scheme (contd.).
Lecture 18 : GA Operator : Selection.
Lecture 19 : GA Operator Selection (Contd.).
Lecture 20 : GA Operator: Crossover techniques.
Lecture 21 : GA Operator : Crossover (Contd.).
Lecture 22 : GA Operator : Crossover (Contd.).
Lecture 23 : GA Operator : Mutation and others.
Lecture 24 : Multi-objective optimization problem solving.
Lecture 25 : Multi-objective optimization problem solving (Contd.).
Lecture 26 : Concept of domination.
Lecture 27 : Non-Pareto based approaches to solve MOOPs.
Lecture 28 : Non-Pareto based approaches to solve MOOPs (Contd.).
Lecture 29 : Pareto-Based approaches to solve MOOPs.
Lecture 30 : Pareto-based approaches to solve MOOPs (contd..).
Lecture 31 : Pareto-based approach to solve MOOPs.
Lecture 32 : Pareto-based approach to solve MOOPs (contd.).
Lecture 33 : Pareto-based approach to solve MOOPs (contd).
Lecture 34 : Introduction to Artificial Neural Network.
Lecture 35 : ANN Architectures.
Lecture 36 : Training ANNs.
Lecture 37 : Training ANNs (Contd..).
Lecture 38 : Training ANNs (Contd..).
Lecture 39 : Training ANNs (Contd..).Lecture 40 : Soft computing tools.
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Syllabus
Overview
INTENDED AUDIENCE: The course is of interdisciplinary nature and students from CSE, IT, EE, ECE, CE,\ME, etc. can take this course.
INDUSTRIES APPLICABLE TO: All IT companies, in general.
COURSE OUTLINE: Soft computing is an emerging approach to computing which parallel 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, soft computing 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 changed scenario and can be implemented with parallel computing. It has enormous applications in many application areas such as medical diagnosis, computer vision, hand written character recondition, pattern recognition, machine intelligence, weather forecasting, network optimization, VLSI design, etc.
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