Generalized Net Model of the Cognitive and Neural Algorithm for Adaptive Resonance Theory 1

• Main Authors: Todor Petkov, Sotir Sotirov
• Format: Article
• Language: English
• Published: Bulgarian Academy of Sciences 2013-12-01
• Series: International Journal Bioautomation
• Subjects: Neural Networks; Generalized Nets; ART1
• Online Access: http://www.biomed.bas.bg/bioautomation/2013/vol_17.4/files/17.4_01.pdf

The artificial neural networks are inspired by biological properties of human and animal brains. One of the neural networks type is called ART [4]. The abbreviation of ART stands for Adaptive Resonance Theory that has been invented by Stephen Grossberg in 1976 [5]. ART represents a family of Neural Networks. It is a cognitive and neural theory that describes how the brain autonomously learns to categorize, recognize and predict objects and events in the changing world. In this paper we introduce a GN model that represent ART1 Neural Network learning algorithm [1]. The purpose of this model is to explain when the input vector will be clustered or rejected among all nodes by the network. It can also be used for explanation and optimization of ART1 learning algorithm.