The prediction of cut-off frequencies of models of gyroelectric waveguides using artificial neural networks

Gyroelectric n and p type's waveguides were usually investigated using differential Maxwell's equations, coupled mode and partial area methods, coherent approaching, least square methods. Computation time of one particular model of this type of waveguide might take quite a long time and even to a couple of days using one of these analytical methods. The whole investigation may require a lot of time in the first stage of research until the right model of waveguide will be found. The artificial neural networks were adjusted for the investigation of gyroelectric n GaAs waveguides. Multilayer perceptron network was selected during investigation. Advantages of artificial neural networks comparing with analytical methods are presented in this paper. The investigation showed that difference between results, obtained using analytical methods, and results, obtained by using artificial neural networks, do not differ by more than 12%. On the other hand prediction using artificial neural networks is performed about 2000 times faster than using traditional methods.

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