Title :
Dynamics of projective adaptive resonance theory model: the foundation of PART algorithm
Author :
Cao, Yongqiang ; Wu, Jianhong
Author_Institution :
Dept. of Cognitive & Neural Syst., Boston Univ., MA, USA
fDate :
3/1/2004 12:00:00 AM
Abstract :
Projective adaptive resonance theory (PART) neural network developed by Cao and Wu recently has been shown to be very effective in clustering data sets in high dimensional spaces. The PART algorithm is based on the assumptions that the model equations of PART (a large scale and singularly perturbed system of differential equations coupled with a reset mechanism) have quite regular computational performance. This paper provides a rigorous proof of these regular dynamics of the PART model when the signal functions are special step functions, and provides additional simulation results to illustrate the computational performance of PART.
Keywords :
ART neural nets; adaptive systems; differential equations; learning (artificial intelligence); pattern clustering; singularly perturbed systems; data clustering; differential equations; neural network; projective adaptive resonance theory; singularly perturbed system; Adaptive systems; Clustering algorithms; Computational modeling; Differential equations; Information technology; Large-scale systems; Mathematics; Neural networks; Pattern recognition; Resonance; Algorithms; Models, Theoretical; Neural Networks (Computer);
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2004.824261