Large-scale pattern storage and retrieval using generalized brain-state-in-a-box neural networks.
In this paper, a generalized Brain-State-in-a-Box (gBSB)-based hybrid neural network is proposed for storing and retrieving pattern sequences. The hybrid network consists of autoassociative and heteroassociative parts. Then, a large-scale image storage and retrieval neural system is constructed using the gBSB-based hybrid neural network and the pattern decomposition concept. The notion of the deadbeat stability is employed to describe the stability property of the vertices of the hypercube to which the trajectories of the gBSB neural system are constrained. Extensive simulations of large scale pattern and image storing and retrieval are presented to illustrate the results obtained.
Oh
Cheolhwan
C
Department of Computer Science, Utah Valley University, Orem, UT 84058, USA. ohch@uvu.edu
Zak
Stanislaw H
SH
D000465:Algorithms; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D016247:Information Storage and Retrieval; D008568:Memory / Q000502:physiology; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D010364:Pattern Recognition, Visual / Q000502:physiology