๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Long term memory storage capacity of multiconnected neural networks

โœ Scribed by P. Peretto; J. J. Niez


Publisher
Springer-Verlag
Year
1986
Tongue
English
Weight
850 KB
Volume
54
Category
Article
ISSN
0340-1200

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


On the Storage Capacity of Nonlinear Neu
โœ Christian Mazza ๐Ÿ“‚ Article ๐Ÿ“… 1997 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 1000 KB

We consider the Hopfield associative memory for storing m patterns xi(r) in { - 1, + 1}(n), r = 1, em leader,m. The weights are given by the scalar product model w(ij)=(m/n)G,i not equal j,w(ii) identical with 0, where G:R --> R is some nonlinear function, like G(x) z.tbnd6; Sgn(x), which is used in

Neural networks of data inhibiting long
โœ Masood A. Badri; Ahmed Al-Mutawa; Amr Murtagy ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 335 KB

We experiment with three neural network models for forecasting to better understand the performance of neural networks for the case when the data exhibits a long memory pattern. To obtain the optimum networks, the effect of network characteristics such as the tratnhtg parameters, the nmnber of ~ lay

Cross-talk theory of memory capacity in
โœ R. J. MacGregor; G. L. Gerstein ๐Ÿ“‚ Article ๐Ÿ“… 1991 ๐Ÿ› Springer-Verlag ๐ŸŒ English โš– 509 KB

The present paper presents a theory for the mechanics of cross-talk among constituent neurons in networks in which multiple memory traces have been embedded, and develops criteria for memory capacity based on the disruptive influences of this cross-talk. The theory is based on interconnection patter

The storage capacity of the complex phas
โœ Zhenxiang Chen; Jianwei Shuai; Jincheng Zheng; Riutang Liu; Boxi Wu ๐Ÿ“‚ Article ๐Ÿ“… 1996 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 321 KB

In this paper, the storage capacity of the Q-state complex phasor neural network is analysed with the signal-to-noise theory. The results indicate that the storage capacity of the model approaches that of the Hopfield model if the number Q is small; while the storage capacity is proportional to Q-2

How embedded memory in recurrent neural
โœ Tsungnan Lin; Bill G. Horne; C.Lee Giles ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 202 KB

Learning long-term temporal dependencies with recurrent neural networks can be a difficult problem. It has recently been shown that a class of recurrent neural networks called NARX networks perform much better than conventional recurrent neural networks for learning certain simple long-term dependen