It is remarkable both because of its effectiveness, in spite of using random matrices to communicate error information, and because it completely removes the taxing requirement of maintaining symmetric weights in a physical neural system.
The performance of the method is analyzed using random matrix theory and information theory.
In this paper, cooperative relay networks with large numbers of nodes are analyzed, and in particular the asymptotic performance improvement of cooperative transmission over direction transmission and relay transmission is analyzed using random matrix theory.
We also see a large gap between the traditional approximation for the effective length in [22] and what is obtained in this article using random matrix theory.
In "Asymptotic analysis of large cooperative relay networks using random matrix theory" by H. Li et al., cooperative relay networks with large number of nodes are analyzed, and in particular the asymptotic performance improvement of cooperative transmission over direct transmission and relay transmission is analyzed using random matrix theory.
In [29], a spectrum sensing technique that relies on the use of multiple receivers to infer on the structure of the received signals using random matrix theory is proposed.
Furthermore, by using random matrix theory for the matrix Φ, we know that (mathbb {E} {|Phi |} le sqrt {n}+sqrt {m}) and (|Phi | le sqrt {n}+sqrt {m}+t) with probability at least (1-2 e^{-t^{2}/2}) for all t≥0 (see, e.g., [30]).
Teschendorff et al. (2011) propose a method for estimating the dimension of a latent surrogate variable using random matrix theory (RMT); application of their algorithm to the matrix is one simple approach for estimating d.
The signal reconstruction precision of current commonly used random matrices which are higher than certainty matrix [3], but the randomness characteristics of random matrices determine that they have complex tectonic process calculation and the limitations of the hardware implementation.
We further investigated microbial assemblage architecture using random-matrix theory based molecular ecological network analyses.
