A neural-network model of the input/output characteristics of a high-power backward wave oscillator

Author

Abdallah, Chaouki ; Yang, Wei ; Schamiloglu, Edl ; Moreland, Laxald D.

Author_Institution

Dept. of Electr. Eng. & Comput. Eng., New Mexico Univ., Albuquerque, NM, USA

Volume

24

Issue

3

fYear

1996

fDate

6/1/1996 12:00:00 AM

Firstpage

879

Lastpage

883

Abstract

This paper discusses an approach to model the input/output characteristics of the Sinus-6 electron beam accelerator-driven backward wave oscillator. Since the Sinus-6 is extremely fast to warrant the inclusion of dynamical effects, and since the sampling interval in the experiment is not fixed, a static continuous neural network model is used to fit the experimental data. Simulation results show that such a simple nonlinear model is sufficient to accurately describe the input/output behavior of the Sinus-6-driven backward wave oscillator (BWO) and that the fitted output waveforms are basically noiseless. This model will be used to control the BWO in order to maximize the radiated power and the efficiency. This paper is also intended to introduce high-power microwave researchers to control concepts that may enhance the outputs of a wide spectrum of sources

Keywords

backward wave oscillators; neural nets; Sinus-6 electron beam accelerator-driven backward wave oscillator; dynamical effects; high-power backward wave oscillator; input/output characteristics; neural-network model; nonlinear model; static continuous neural network model; Acceleration; Chaos; Electron beams; Helium; Mathematical model; Microwave oscillators; Neural networks; Robust control; Sampling methods; Switches;

fLanguage

English

Journal_Title

Plasma Science, IEEE Transactions on

Publisher

ieee

ISSN

0093-3813

Type

jour

DOI

10.1109/27.533091

Filename

533091