Introduction to Soft Computing (Prof. DebasisSamanta, IIT Kharagpur) | Computer Science

Introduction to Soft Computing

Introduction to Soft Computing. Instructor: Prof. DebasisSamanta, Department of Computer Science and Engineering, IIT Kharagpur. Soft computing is an emerging approach to computing which 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, ante'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 scenarios and can be implemented with parallel computing. It has enormous applications in many application areas such as medical diagnosis, computer vision, hand-written character recognition, pattern recognition, machine intelligence, weather forecasting, network optimization, VLSI design, etc. (from nptel.ac.in)

Introduction

Lecture 01 - Introduction

Lecture 02 - Introduction to Fuzzy Logic

Lecture 03 - Fuzzy Membership Functions and Defining Membership Functions

Lecture 04 - Fuzzy Operations

Lecture 05 - Fuzzy Relations

Lecture 06 - Fuzzy Relations (cont.), Fuzzy Propositions

Lecture 07 - Fuzzy Implications

Lecture 08 - Fuzzy Inferences

Lecture 09 - Defuzzification Techniques

Lecture 10 - Defuzzification Techniques (cont.)

Lecture 11 - Fuzzy Logic Controller

Lecture 12 - Fuzzy Logic Controller (cont.)

Lecture 13 - Fuzzy Logic Controller (cont.)

Lecture 14 - Concept of Genetic Algorithm

Lecture 15 - Concept of Genetic Algorithm (cont.), GA Strategies

Lecture 16 - GA Operator: Encoding Schemes

Lecture 17 - GA Operator: Encoding Schemes (cont.)

Lecture 18 - GA Operator: Selection

Lecture 19 - GA Operator: Selection (cont.)

Lecture 20 - GA Operator: Crossover Techniques

Lecture 21 - GA Operator: Crossover Techniques (cont.)

Lecture 22 - GA Operator: Crossover Techniques (cont.)

Lecture 23 - GA Operator: Mutation and Others

Lecture 24 - Multi-objective Optimization Problem Solving

Lecture 25 - Multi-objective Optimization Problem Solving (cont.)

Lecture 26 - Concept of Domination

Lecture 27 - Non-pareto based Approaches to Solve MOOPs

Lecture 28 - Non-pareto based Approaches to Solve MOOPs (cont.)

Lecture 29 - Pareto based Approaches to Solve MOOPs

Lecture 30 - Pareto based Approaches to Solve MOOPs (cont.)

Lecture 31 - Pareto based Approaches to Solve MOOPs (cont.)

Lecture 32 - Pareto based Approaches to Solve MOOPs (cont.)

Lecture 33 - Pareto based Approaches to Solve MOOPs (cont.)

Lecture 34 - Introduction to Artificial Neural Network

Lecture 35 - ANN Architectures

Lecture 36 - Training ANNs

Lecture 37 - Training ANNs (cont.)

Lecture 38 - Training ANNs (cont.)

Lecture 39 - Training ANNs (cont.)

Lecture 40 - Soft Computing Tools

References

Introduction to Soft Computing
Instructor: Prof. DebasisSamanta, Department of Computer Science and Engineering, IIT Kharagpur. Soft computing is an emerging approach to computing which parallels the remarkable ability of the human mind to reason and learn in an environment of uncertainty and imprecision.