Using recurrent neural networks for adaptive communication channelequalization

Nonlinear adaptive filters based on a variety of neural network models have been used successfully for system identification and noise-cancellation in a wide class of applications. An important problem in data communications is that of channel equalization, i.e., the removal of interferences introduced by linear or nonlinear message corrupting mechanisms, so that the originally transmitted symbols can be recovered correctly at the receiver. In this paper we introduce an adaptive recurrent neural network (RNN) based equalizer whose small size and high performance makes it suitable for high-speed channel equalization. We propose RNN based structures for both trained adaptation and blind equalization, and we evaluate their performance via extensive simulations for a variety of signal modulations and communication channel models. It is shown that the RNN equalizers have comparable performance with traditional linear filter based equalizers when the channel interferences are relatively mild, and that they outperform them by several orders of magnitude when either the channel's transfer function has spectral nulls or severe nonlinear distortion is present. In addition, the small-size RNN equalizers, being essentially generalized IIR filters, are shown to outperform multilayer perceptron equalizers of larger computational complexity in linear and nonlinear channel equalization cases.     

REENGINEERING DONE RIGHT Intermediate Solutions That Are Cost-Effective

Effective systems development techniques attract a lot of attention, but little has been done to effectively address systems maintenance. This article describes an effective, proven approach to resurrecting existing systems and Increasing their useful life, using a system that employs both software tools and a rigorous methodology.     

Algebra of algorithms and Kaluzhnin's graphschemas

This research was supported under project No. 6.02.02/128-93 as part of the state scientific-technical program on future information technologies and systems by the Ukrainian State Committee, of Science and Technology.     

Comments on “Editorial: fuzzy models-what are they andwhy?”

In the editorial by J.C. Bezdek (ibid., p.1), an example is presented to demonstrate differences between fuzzy membership and probability. The authors argue that probability can be used in a way much more closely analogous to this use of fuzzy membership, weakening the argument for the latter     

Synergistic interactions of etoposide and interleukin-1 alpha are not due to DNA damage in human melanoma cells

Family nursing, always a component of nursing, has been recently receiving increased attention through publications, educational programs, clinical practice settings and family nursing research. Nurses are in the best position to assist families experiencing the illness of a loved one, but often lack the skills and confidence necessary to assist with the psychosocial needs. The purpose of this paper is to describe the development and implementation of a family nursing program within a large, tertiary care facility. The program's evaluation is ongoing but early outcomes indicate that day-to-day nursing practice has been positively influenced and improved job satisfaction may result.