๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Neural network models of nuclear systematics

โœ Scribed by K.A. Gernoth; J.W. Clark; J.S. Prater; H. Bohr

Publisher
Elsevier Science
Year
1993
Tongue
English
Weight
570 KB
Volume
300
Category
Article
ISSN
0370-2693

No coin nor oath required. For personal study only.

๐Ÿ“œ SIMILAR VOLUMES

Nuclear mass systematics using neural ne
Nuclear mass systematics using neural networks
โœ S. Athanassopoulos; E. Mavrommatis; K.A. Gernoth; J.W. Clark ๐Ÿ“‚ Article ๐Ÿ“… 2004 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 315 KB

New global statistical models of nuclidic (atomic) masses based on multilayered feedforward networks are developed. One goal of such studies is to determine how well the existing data, and only the data, determines the mapping from the proton and neutron numbers to the mass of the nuclear ground sta

Modelling neural networks
Modelling neural networks
โœ Bikas K. Chakrabarti; Prabir K. Dasgupta ๐Ÿ“‚ Article ๐Ÿ“… 1992 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 720 KB

We review here briefly some of our recent studies on neural network modelling. We discuss the studies on relaxation and growth of correlation in the Hopfield model, increase in memory loading capacity with an extended Hopfield-like model with delayed dynamics, the prediction capability of time serie