scholarly journals Neural network approach to the prediction of seismic events based on low-frequency signal monitoring of the Kuril-Kamchatka and Japanese regions

2013 ◽  
Vol 56 (3) ◽  
Author(s):  
Irina Popova ◽  
Alexander Rozhnoi ◽  
Maria Solovieva ◽  
Boris Levin ◽  
Masashi Hayakawa ◽  
...  
Keyword(s):  
Neural Network ◽  
Seismic Event ◽  
Back Propagation ◽  
Low Frequency ◽  
Signal Amplitude ◽  
Seismic Events ◽  
Network Approach ◽  
Neural Network Approach ◽  
Propagation Technique ◽  
Japanese Regions

<p>Very-low-frequency/ low-frequency (VLF/LF) sub-ionospheric radiowave monitoring has been widely used in recent years to analyze earthquake preparatory processes. The connection between earthquakes with M ≥5.5 and nighttime disturbances of signal amplitude and phase has been established. Thus, it is possible to use nighttime anomalies of VLF/LF signals as earthquake precursors. Here, we propose a method for estimation of the VLF/LF signal sensitivity to seismic processes using a neural network approach. We apply the error back-propagation technique based on a three-level perceptron to predict a seismic event. The back-propagation technique involves two main stages to solve the problem; namely, network training, and recognition (the prediction itself). To train a neural network, we first create a so-called ‘training set’. The ‘teacher’ specifies the correspondence between the chosen input and the output data. In the present case, a representative database includes both the LF data received over three years of monitoring at the station in Petropavlovsk-Kamchatsky (2005-2007), and the seismicity parameters of the Kuril-Kamchatka and Japanese regions. At the first stage, the neural network established the relationship between the characteristic features of the LF signal (the mean and dispersion of a phase and an amplitude at nighttime for a few days before a seismic event) and the corresponding level of correlation with a seismic event, or the absence of a seismic event. For the second stage, the trained neural network was applied to predict seismic events from the LF data using twelve time intervals in 2004, 2005, 2006 and 2007. The results of the prediction are discussed.</p>

scholarly journals The Behavior of VLF/LF Variations Associated with Geomagnetic Activity, Earthquakes, and the Quiet Condition Using a Neural Network Approach

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 691 ◽  
Author(s):  
Irina Popova ◽  
Alexandr Rozhnoi ◽  
Maria Solovieva ◽  
Danila Chebrov ◽  
Masashi Hayakawa
Keyword(s):  
Neural Network ◽  
Radio Wave ◽  
Seismic Activity ◽  
Low Frequency ◽  
Magnetic Storms ◽  
Network Approach ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Different Types ◽  
Parameters Of Seismicity

The neural network approach is proposed for studying very-low- and low-frequency (VLF and LF) subionospheric radio wave variations in the time vicinities of magnetic storms and earthquakes, with the purpose of recognizing anomalies of different types. We also examined the days with quiet geomagnetic conditions in the absence of seismic activity, in order to distinguish between the disturbed signals and the quiet ones. To this end, we trained the neural network (NN) on the examples of the representative database. The database included both the VLF/LF data that was measured during four-year monitoring at the station in Petropavlovsk-Kamchatsky, and the parameters of seismicity in the Kuril-Kamchatka and Japan regions. It was shown that the neural network can distinguish between the disturbed and undisturbed signals. Furthermore, the prognostic behavior of the VLF/LF variations indicative of magnetic and seismic activity has a different appearance in the time vicinity of the earthquakes and magnetic storms.

scholarly journals The method of array antenna constructive synthesis on the basis of neural network approach

2019 ◽  
Vol 30 ◽  
pp. 05001
Author(s):  
Sergey Mishchenko ◽  
Vitaliy Shatskiy ◽  
Alexey Litvinov ◽  
Denis Eliseev
Keyword(s):  
Neural Network ◽  
Active Element ◽  
Back Propagation ◽  
Array Antenna ◽  
Antenna Element ◽  
Network Approach ◽  
Passive Element ◽  
Neural Network Approach ◽  
Neural Network Learning ◽  
Error Back Propagation

The method to decision constructive synthesis of array antennas was conducted. The method usefull when antenna elements can be in discreste states (for example: active element, passive element, excluded item, active element with discrete nominal of output power e.t.c). The method is based on neural network approach. The structure of a neural network consist of a classifying neural network and several approximating neural networks is substantiated. Input signals correspond to phase centers of array antenna elements. Number of output signals in classifying part is equal to discrete status of antenna element. Each approximating part of network has one output signal wich correspond to continious meaning. Separate parts of network preliminary learning with error back propagation method. The genetic algorithm of neural network learning with limited number of training coefficients is proposed. Examples of solving problems of constructive synthesis, with different indicators of the quality of neural network training are given.

Back Propagation Method of Artificial Neural Networks for Finding the Position Control of Stanford Manipulator and Direct Kinematic Analysis of Elbow Manipulator

2018 ◽  
Vol 7 (1) ◽  
pp. 46 ◽  
Author(s):  
M. Sailaja ◽  
R. D. V. Prasad
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Inverse Kinematics ◽  
Back Propagation ◽  
Network Approach ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Direct Kinematics

Nowadays the robot technology is advancing rapidly and the use of robots in industries has been increasing. In designing a robot manipulator, kinematicsplays a vital role. The kinematic problem of manipulator control is divided into two types, direct kinematics and inverse kinematics. Robot inverse kinematics, which is important in robot path planning, is a fundamental problem in robotic control. Past solutions for this problem have been through the use of various algebraic or algorithmic procedures, which may be less accurate and time consuming. Artificial neural networks have the ability to approximate highly non-linear functions applied in robot control. The neural network approach deserves examination because of the fundamental properties of computation speed, and they can generalize untrained solutions. In the present work an attempt has been made to evaluate the problemof robot inverse kinematics of Stanford manipulator using artificial neural network approach. Finally two programs are written using C language to solve inverse kinematic problem of Stanford manipulator using Back propagation method of artificial neural network. In this network, the input layer has six nodes, the hidden layer has three nodes, and the output layer has two nodes. And also Elbow manipulator was modelled and its direct kinematics was analysed.

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