Incremental communication for adaptive resonance theory networks.
We have proposed earlier the incremental internode communication method to reduce the communication cost as well as the time of the learning process in artificial neural networks (ANNs). In this paper, the limited precision incremental communication method is applied to a class of recurrent neural networks, the adaptive resonance theory 2 (ART2) networks. Simulation studies are carried out to examine the effects of the incremental communication method on the convergence behavior of ART2 networks. We have found that, 7-13-b precision is sufficient to obtain almost the same results as those with full (32-b) precision conventional communication. A theoretical error analysis is also carried out to analyze the effects of the limited precision incremental communication. The simulation and analytical results show that the limited precision errors are bounded and do not seriously degrade the convergence of ART2 networks. Therefore, the incremental communication can be incorporated in parallel and special-purpose very large scale integration (VLSI) implementations of the ART2 networks.
Chen
Ming
M
Faculty of Computer Science, University of New Brunswick, Fredericton, NB, E3B 5A3, Canada.
Ghorbani
Ali A
AA
Bhavsar
Virendrakumar C
VC
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003205:Computing Methodologies; D005246:Feedback; D016247:Information Storage and Retrieval / Q000379:methods*; D007257:Information Theory; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted