Multilayer optical learning networks

Multilayer optical learning networks.

A new approach to learning in a multilayer optical neural network based on holographically interconnected nonlinear devices is presented. The proposed network can learn the interconnections that form a distributed representation of a desired pattern transformation operation. The interconnections are formed in an adaptive and self-aligning fashioias volume holographic gratings in photorefractive...

Estimating CapEx in Optical Multilayer Networks

A model for calculating the costs and quantities of required components of a multilayer transport network comprising IP/MPLS-over-SDH/OTN-over-WDM layers is presented. From that a method to estimate the capital expenditures is presented. The method is based on analytical approximations and does not depend on the network topology. Results are presented showing good agreement between the estimati...

Bayesian Learning of Dynamic Multilayer Networks

A plethora of networks is being collected in a growing number of fields, including disease transmission, international relations, social interactions, and others. As data streams continue to grow, the complexity associated with these highly multidimensional connectivity data presents novel challenges. In this paper, we focus on the time-varying interconnections among a set of actors in multiple...

Selective Learning for Multilayer Feedforward Neural Networks

Optimal learning in multilayer neural networks

The generalization performance of two learning algorithms, Bayes algorithm and the ‘‘optimal learning’’ algorithm, on two classification tasks is studied theoretically. In the first example the task is defined by a restricted two-layer network, a committee machine, and in the second the task is defined by the so-called prototype problem. The architecture of the learning machine, in both cases, ...