Abstract
Adaptive Resonance Theory (ART) represents a powerful neural network architecture designed to address the stability-plasticity dilemma. Its primary objective is to enable rapid learning without compromising retention. ART embodies characteristics of self-organization and self-stabilization, distinguishing it from traditional neural networks. Unlike error-based learning prevalent in conventional models, ART employs competitive learning mechanisms. One of the distinguishing features of ART is its involvement in hypothesis testing, a departure from the approach of deep learning. This capability allows ART to learn dynamically in real-time environments. Over time, various ART models have emerged that cater to different types of data, such as binary and analog. This research proposal aims to explore the artlib library in Python, implementing various ART models on different datasets to analyze and compare their performance with more conventional algorithms like Self-Organizing Maps and K-Means clustering. This study seeks to highlight their efficacy, practical implementation, and differences between these frameworks.
Library of Congress Subject Headings
Self-organizing maps; Machine learning; Neural networks (Computer science)
Publication Date
5-2025
Document Type
Thesis
Student Type
Graduate
Degree Name
Computer Science (MS)
Department, Program, or Center
Computer Science, Department of
College
Golisano College of Computing and Information Sciences
Advisor
Alexander Ororbia
Advisor/Committee Member
Weijie Zhao
Advisor/Committee Member
Eduardo Lima
Recommended Citation
Dixit, Meeti, "Resonating Patterns: Adaptive Resonance Theory and Self-Organizing Maps" (2025). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/12091
Campus
RIT – Main Campus
Plan Codes
COMPSCI-MS