Fuzzy Logic Systems

This is a series of videos about Fuzzy Logic Systems, which will cover the working principle of fuzzy inference systems; the fundemental structure (fuzzifier, knowledge base/rule base, fuzzy inference engine and defuzzifier); techniques of fuzzification and defuzzification; general structure of knowledge/rule base; meachanism of fuzzy inferencing; introduction to three fuzzy logic systems, namely, Mamdani, Sugeno and Tsukamoto fuzzy inference systems.

Lecture Slides: Introduction to Fuzzy Inference Systems

Part 1: Introduction to Fuzzy Inference Systems

Part 2: Fuzzy Inference Systems

Part 3: Structure of Fuzzy Inference Systems - Fuzzifier

Part 4: Structure of Fuzzy Inference Systems - Knowledge Based and Fuzzy Inference Engine

Part 5: Structure of Fuzzy Inference Systems - Defuzzification

Part 6:  Three Fuzzy Inference Systems: Mamdani, Sugeno and Tsukamoto fuzzy inference systems

Binary Genetic Algorithm (GA)

This is a series of videos about binary genetic algorithm (GA), which will cover the working concepts and principles and the details of each components of binary GA (i.e., coding/encoding prcoess for binary representation, decision variables, cost functions, population, natural selection, selection, genetic operations (crossover and mulation), stopping criteria, performance evaluation, benchmark functions, schema theorem), using flowcharts, diagrams, animations and examples.

Part 1: Introduction of Binary Genetic Algorithm

Part 2: General Working Principle and Coding/Encoding of Binary Genetic Algorithm

Part 3: Decision Variables, Cost Functions and Population and Natural Selection Process of Binary Genetic Algorithm

Part 4: Selection Processes (Pairing, Random Pairing, Cost Weighting and Tournament) of Binary Genetic Algorithm

Part 5: Crossover and Mutation of Binary Genetic Algorithm

Part 6:  Stopping Criteria, Performance Evaluation and Benchmark Functions of Binary Genetic Algorithm

Part 7: Why do GAs work, Schema Theorem of Binary Genetic Algorithm

Numerical Optimisation Techniques

This is a series of videos about traditional numerical optimisation techniques that the working concepts and principles will be explained through using flowcharts, diagrams, animations and examples.

Least Squares Method

Linear Programming - Explanation and Example

Nelder-Mead Downhill Simplex Method (2 dimensions) + A numerical Example

Gradient Descent Algorithm

Line Minimization Algorithm