Fuzzy ARTMAP is a neural network architecture that uses fuzzy logic and adaptive resonance theory (ART) for supervised learning. It incorporates two fuzzy ART modules, ART-a and ART-b, linked together by an inter-ART module called the MAP field. This allows the network to form predictive associations between categories and track matches using a mechanism called match tracking. The match tracking recognizes category structures to avoid repeating predictive errors on subsequent inputs. Fuzzy ARTMAP is trained until it can correctly classify all training data by increasing the vigilance parameter of ART-a in response to predictive mismatches at ART-b.

1. Simplified Fuzzy
ARTMAP
Presented by : Pradip Sanjay Bankar
(BE19F06F004)

2. What is Fuzzy Logic?
● The term “Fuzzy Logic” refers to the logic of approximation.
● Boolean logic assumes that every fact is either completely true or false.
● Fuzzy logic allows varying degrees of truth.
● Computers can apply this logic to represent vague and imprecise ideas like
hot, tall, balding, etc.

3. Crisp vs Fuzzy result

4. What is ART Architecture?
● ART refers to Adaptive Resonance Theory.
● Adaptive - Open to new learning and Resonance - without losing old
information.
● Accepts an input vector and classify it to one of the categories.
● The category it belongs to is decided by the one the input vector it most
resembles to.
● If the input does not match to any of the existing categories, a new category
is created.

5. Simplified ART Module

6. What is Fuzzy ARTMAP?
● Fuzzy ARTMAP is a neural network architecture for conducting supervised
learning in multidimensional setting.
● When a fuzzy ARTMAP is used in learning model, it’s trained till it classifies
all the training data correctly.

7. Fuzzy ARTMAP

8. Let’s understand Fuzzy ARTMAP
● It incorporates two fuzzy ART Modules ART-a and ART-b linked together via
inter-ART Module called MAP field.
● Map field is used to form predictive associations between categories and to
realize the match tracking rule. Whereby, the vigilance parameter of ART-a
increases in response to predictive mismatch at ART-b.
● Match tracking recognizes categories structure so that predictive error is
not repeated on subsequent presentations of input.

9. Thank you