Learning and generation of long-range correlated sequences

Details Learning and generation of long-range correlated sequences Learning and generation of long-range correlated sequences

2000-07-05

arxiv.org/abs/cond-mat/0007075v1

Physical Review E 62(2) (2000)

Physics

Condensed Matter

Disordered Systems and Neural Networks

5 pages, 3 figures, accepted for publication in Physical Review E

Scientific paper

10.1103/PhysRevE.62.1617

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.

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