Self Organizing Maps (SOM) technique was developed in 1982 by a professor, Tuevo Kohonen. Professor Kohonen worked on auto-associative memory during the 1970s and 1980s and in 1982 he presented his self-organizing map algorithm. SOMs are named as “Self-Organizing” because

## Unsupervised Learning

A cluster is comprised of a number of similar feature vectors group together. If there are ‘d‘ features used to describe the data, a cluster can be described as a region in d-dimensional space containing a relatively high density of

## Operations on Fuzzy Set

Operations on Fuzzy Set 1. Subset A ⊂ B ↔ μA(x) ≤ μB(x), ∀ x ∈ X 2. Complement Ac ↔ μAc(x) = 1 − μA(x), ∀ x ∈ X 3. Superset A ⊃ B ↔ μA(x) ≥ μB(x), ∀ x ∈ X The characteristic function will never exceed

## Fuzzification | Membership Functions

1. Triangular MF 2. Trapezoidal MF 3. Gaussian MF 4. Generalized bell MF 5. Sigmoidal MF

## Fuzzy T-norm and S-norm Operator

T-norm (Triangular norm) ——> Fuzzy Intersection T-norm operator: A∩B ↔ µA∩B (x) = T (µA(x), µB(x)) = µA(x) ∧ µB(x), ∀ x ∈ X where ∧ is for T-norm operator (example, min. product) Definition of T-norm operator A T-norm operator denoted

## Genetic Algorithm | Advantages & Disadvantages

Genetic Algorithm (GA) Genetic Algorithms are the heuristic search and optimization techniques that mimic the process of natural evolution. A genetic algorithm is a local search technique used to find approximate solutions to Optimisation and search problems. It is an

## SoftComputing and HardComputing

SoftComputing It is a collection of methodologies, which aim to exploit tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness, and low solution cost. SoftComputing Main Components 1. Approximate Reasoning example: Probabilistic Reasoning, Fuzzy logic. 2. Search and