Title :
A Simulation Study of Deep Belief Network Combined with the Self-Organizing Mechanism of Adaptive Resonance Theory
Author :
Wu, Yan ; Cai, H.J.
Author_Institution :
Int. Sch. of Software, Wuhan Univ., Wuhan, China
Abstract :
Computer simulation study of brain neuronal networks is an active academic field. Deep Belief Network (DBN) introduces an effective way of training deep neural networks and the Adaptive Resonance Theory (ART) puts forward a two-layer competitive network emulating human cognitive processes. In our study, we implement a DBN with the mechanism of ART which benefits from DBN´s multi-layer structure and ART´s self-organizing stable learning mechanism. Our preliminary results show that the optimal number of layers is relevant to the data learned. The correct reconstruction rate decreases slowly with respect to the volume of data stored.
Keywords :
ART neural nets; belief networks; cognitive systems; learning (artificial intelligence); self-organising feature maps; adaptive resonance theory; brain neuronal network; deep belief network; deep learning; deep neural network training; human cognitive process; machine learning; multilayer structure; reconstruction rate; self-organizing stable learning; two-layer competitive network; Adaptation model; Associative memory; Biological neural networks; Biological system modeling; Brain modeling; Subspace constraints; Training;
Conference_Titel :
Computational Intelligence and Software Engineering (CiSE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5391-7
Electronic_ISBN :
978-1-4244-5392-4
DOI :
10.1109/CISE.2010.5677265
