ART, ARTMAP, Fuzzy ART Neural Network Implementations

These MATLAB-developed programs for ART, ARTMAP, and Fuzzy ART networks serve as valuable learning references for understanding ART-based neural networks. The implementations are grounded in Adaptive Resonance Theory (ART) neural network principles, featuring core algorithmic components such as vigilance parameter tuning, category selection mechanisms, and resonance matching processes. Through code-level analysis and experimentation with these programs, researchers can gain deeper insights into neural network operations, including pattern recognition workflows, stability-plasticity tradeoffs, and real-time learning capabilities. The code structure demonstrates key functions like similarity calculation, category prototype updating, and mismatch reset operations. Furthermore, these programs provide practical tools for studying ART neural networks through simulations that validate theoretical performance aspects such as clustering accuracy, learning efficiency, and scalability. The implementations support experimental verification of network behavior under various parameter configurations, contributing to the advancement and practical application of ART neural network technologies.