Learning and generation of long-range correlated sequences
被引:1
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作者:
Priel, A
论文数: 0引用数: 0
h-index: 0
机构:
Bar Ilan Univ, Minerva Ctr, IL-52900 Ramat Gan, IsraelBar Ilan Univ, Minerva Ctr, IL-52900 Ramat Gan, Israel
Priel, A
[1
]
Kanter, I
论文数: 0引用数: 0
h-index: 0
机构:Bar Ilan Univ, Minerva Ctr, IL-52900 Ramat Gan, Israel
Kanter, I
机构:
[1] Bar Ilan Univ, Minerva Ctr, IL-52900 Ramat Gan, Israel
[2] Bar Ilan Univ, Dept Phys, IL-52900 Ramat Gan, Israel
来源:
PHYSICAL REVIEW E
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2000年
/
62卷
/
02期
关键词:
D O I:
10.1103/PhysRevE.62.1617
中图分类号:
O35 [流体力学];
O53 [等离子体物理学];
学科分类号:
070204 ;
080103 ;
080704 ;
摘要:
We study the capability to learn and to generate long-range, power-law correlated sequences by a fully connected asymmetric network. The focus is set on the ability of neural networks to extract statistical features from a sequence. We demonstrate that the average power-law behavior is learnable, namely, the sequence generated by the trained network obeys the same statistical behavior. The interplay between a correlated weight matrix and the sequence generated by such a network is explored. A weight matrix with a power-law correlation function along the vertical direction, gives rise to a sequence with a similar statistical behavior.