Title :
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
fDate :
6/1/1996 12:00:00 AM
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;
Journal_Title :
Plasma Science, IEEE Transactions on
