Abstract
It is well known that the electromagnetic (EM) waves that radiate from the earth's crust are useful for predicting earthquakes. We observe electromagnetic waves in the extremely low frequency (ELF) band of 223Hz. These observed signals contain several undesired signals due to fluctuations in the magnetosphere or the ionized layer, lightning radiation from the tropics, and so on. We proposed a multi-layer neural network (NN) with a compressed data. The proposed NN is useful for precursor signal detection, however, detection sensitivity is not discussed enough. In this paper, the artificial signal is used for obtaining a detection rate. This uses as a parameter to avoid an over-fitting. Moreover, the uniform random number (RN) is added to input signal for improving generalization capability. It is shown that proposed NN is effective to improve a generalization capability of the problem of a precursor signal detection.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
