Abstract
In the framework of the complex-valued neural networks dealing with phase values adaptively, we can realize various adaptive subsystems required in optical communications such as a learning phase equalizer. Modern optical communications attains a high degree of development mainly in trunk lines. Moreover, near-future networks provide subscribers with high-speed and multichannel information transmission over all-optical routers and switches. Thereby, we have to compensate the fiber dispersion varying with successively switched optical routes. The dispersion variation is very large since the high-speed multichannel optical communications occupies a wide frequency bandwidth. The optical-phase equalizer to be presented in this chapter can be one of the principles useful in such applications. As an example, we consider a system with supervised learning here.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Hirose, A. (2012). Adaptive Optical-Phase Equalizer. In: Complex-Valued Neural Networks. Studies in Computational Intelligence, vol 400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27632-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-27632-3_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27631-6
Online ISBN: 978-3-642-27632-3
eBook Packages: EngineeringEngineering (R0)