in the second case, s 2.2, H s 1.5 mm. We can use the r neural network independently of the optimization phase. Now that the optimization of the antenna has begun, the GA can be used with the neural network in the variation range. Parameters of the genetic algorithm: ⅷ Population: 20 chromosomes
Deterministic effective equations for the propagation of expectation in noisy nonlinear optical fibers
✍ Scribed by L. Barletti; G. Busoni
- Publisher
- John Wiley and Sons
- Year
- 2010
- Tongue
- English
- Weight
- 121 KB
- Volume
- 33
- Category
- Article
- ISSN
- 0170-4214
- DOI
- 10.1002/mma.1323
No coin nor oath required. For personal study only.
✦ Synopsis
Abstract
We consider electromagnetic propagation in optical fibers as described by a nonlinear Schrödinger equation. The equation is endowed with input data representing a signal affected by a Gaussian white noise. Assuming both the nonlinearity and the noise to be small of the same order ε, we derive a hierarchy, at various orders in ε, of deterministic effective equations for the expectation of the propagated signal. Copyright © 2010 John Wiley & Sons, Ltd.
📜 SIMILAR VOLUMES
In this paper, we present solutions for the nonlinear Schro ¨dinger (NLS) equation with spatially inhomogeneous nonlinearities describing propagation of light in nonlinear media, under two sets of transverse modulation forms of inhomogeneous nonlinearity. The bright soliton solution and Gaussian sol
## Abstract In this letter, we propose an analytic model for performance analyses of the optical subcarrier‐multiplexing (SCM) fiber‐optic link for CDMA RF signal transmission in mobile communication networks. We present optimal operational conditions, taking account of the nonlinear effects of the