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In order to search the abnormal data of seismic satellite quickly, we extract the ultra low frequency electric field waveform data of 10 days before the Wenchuan earthquake in this paper. The back propagation neural network classification model is designed and the self-organizing feature map network clustering model is used to verify back propagation network using the mean, variance, skewness and...
In order to search of ionospheric anomaly information before the Wenchuan earthquake, this paper selects 10 days DEMETER satellite field data before Wenchuan earthquake as the research object and select mean, variance, skewness and kurtosis four kinds of random signal digital features as the input layer. After a number of samples training, Self-Organizing Map neural network clustering model is established...
In order to achieve the fast classification for Ultra-low-frequency (ULF) electron field data in the Space, this paper designs an electric field classifier based on the back-propagation (BP) neural network with extracting the ULF section electric field waveform data of the Wenchuan earthquake, using the statistical methods to obtain four characteristics of the mean value, mean square error, skewness...
By analyzing superior and inferior events of college and university attended in group a of Beijing college and university track and field meeting held during 7 years as research object. The finding illustrate that game theory have an effect by wise choosing on potential events, and such a characteristic employed in specialized training got a good achievement. College or university should put more...
Combining GIS and RS technique in the study of ecological vulnerability assessment in Zhalong Wet Ecosystem, an improved Back-Propagation neural network was established to model and evaluate ecological vulnerability based on Press-State-Response model. The research educed that: the regions of moderate or severe vulnerability accounted for 18% while the regions of potential or minute vulnerability...
Support Vector Machine (SVM) is based on statistical learning theory which developed from the common machine learning. It is an effective tool to deal with limited samples. This paper proposes a model of the dissolved gas analysis (DGA) of transformer based on Multi-class SVM. Firstly, with the combination of SVM multi-class classification methods one-versus-rest (1-v-r) and one-versus-one (1-v-1),...
Rough Set Theory has been widely used in pattern recognition. In this paper, the rough set theory has been applied to the intrusion detection. An effective method based rough set for anomaly intrusion detection with low overhead and high efficiency has been presented. The method is based on Rough Set Theory to extract a set of detection rules with a minimal size as the normal behavior model from the...
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