Adaptive Resonance Theory (ART) is a neural network model developed by Stephen Grossberg and Gail Carpenter to explain how the brain processes information. ART networks use unsupervised and supervised learning and can perform tasks like pattern recognition and prediction. The basic ART system consists of a comparison field, recognition field, vigilance parameter, and reset module. The recognition field contains neurons that match the input vector. If the best matching neuron exceeds the vigilance parameter, learning occurs via resonance; otherwise, the neuron is inhibited and a new match is sought. ART has applications in areas like face recognition, image compression, and medical diagnosis.

2. Adaptive Resonance
Theory
Presented By:
D Surat
M.Sc. Physics (Elec.)
Dayalbagh educational Institute

3. Outline
● Adaptive Resonance Theory basics
● ART Classification
● ART Networks
● Basic ART Network Architecture
● ART Algorithm

4. ART:Basics
• ART stands for Adaptive resonance theory.
• developed by Stephen Grossberg and Gail Carpenter on
aspects of how the brain processes information.
• It describes a number of neural network models which
use supervised and unsupervised learning methods.
• Address problems such as pattern recognition and
prediction.

5. ART:Basics(Cont’d)
• The term “resonance” refers to a resonant state of a
neural network in which a category prototype vector
matches close enough to the current input vector. ART
matching leads to this resonant state, which permits
learning. The network learns only in its resonant state.

6. ART Classification

7. • ART 1 :
▫ simplest variety of ART networks
▫ accepting only binary inputs.
• ART2 :
▫ support continuous inputs.
• ART3 is refinement of both models.
• Fuzzy ART implements fuzzy logic into ART’s pattern recognition.
• ARTMAP also known as Predictive ART, combines two slightly
modified ART-1 or ART-2 units into a supervised learning structure .
• Fuzzy ARTMAP is merely ARTMAP using fuzzy ART units, resulting
in a corresponding increase in efficacy.

8. ART Networks
• The basic ART system is unsupervised learning model. It
typically consists of
1. a comparison field
2. a recognition field composed of neurons,
3. a vigilance parameter, and
4. a reset module

9. • Comparison field: The comparison field takes an input
vector and transfer it to its best match in the
recognition field. Its best match is the single neuron
whose set of weights most closely matches the input
vector.
• Recognition field: Each recognition field neuron, outputs
a negative signal proportional to that neuron’s quality of
match to the input vector to each of the other
recognition field neurons and inhibits their output
accordingly

10. • Vigilance parameter: After the input vector is
classified, a reset module compares the strength of the
match to a vigilance parameter. The vigilance parameter
has cansidrable influence on the system:
- Higher vigilance produces highly detailed memories.
- lower vigilance results in more general memories.
• Reset module: The reset module compares the strengh
of the recognition match to the vigilance parameter.
- if the vigilance threshold is met, then training
commences.
- otherwise, if the match level does not meet the vigilance
parameter, then the firing recognition neuron is inhibited
until a new input vector is applied.

11. Basic ART Network Architecture

12. ART Algorithm
• Input is presented (in layer 1).
•Forward transmission via bottom-up weights(Inner
product) •
Best matching node fires (winner-take-all layer)
Comparison Phase (in Layer 1)
• Backward transmission via top-down weights
• Vigilance test: class template matched with input
pattern. If pattern close enough to template,
categorisation was successful and “resonance” achieved

13. • If not close enough reset winner neuron and try next
best matching • (The reset inhibit the current winning
neuron, and the current expectation is removed)
• A new competition is then performed in Layer 2, while
the previous winning neuron is disable.
• The new winning neuron in Layer 2 projects a new
expectation to Layer 1, through the L2-L1 connections.
• This process continues until the L2-L1 expectation
provides a close enough match to the input pattern.
• The process of matching, and subsequent adaptation, is
referred to as resonance

14. Summary

17. Example
cluster of the vectors
11100,11000,00001,00011
low vigilance: 0.3
High vigilance: 0.7

18. With ρ=0.3

19. With ρ=0.7

20. Applications
• Face recognition
• Image compression
• Mobile robot control
• Target recognition
• Medical diagnosis
• Signature verification

21. Reference
● https://en.wikipedia.org/wiki/Adaptive_resonance_theory
● http://www.myreaders.info/05-
Adaptive_Resonance_Theory.pdf
● http://vp.dei.ac.in:8081/data/NN/presentations/PHM802_NN_
LP11.pdf

22. Thank You !!