scholarly journals Threat prediction in complex distributed systems using artificial neural network technology

2020 ◽  
Author(s):  
Evgeny Palchevsky ◽  
Olga Khristodulo ◽  
Sergey Pavlov
Keyword(s):  
Neural Network ◽  
Distributed Systems ◽  
Flood Control ◽  
Least Squares Method ◽  
Mathematical Formulation ◽  
Point Of View ◽  
Water Levels ◽  
Control Measures ◽  
Forecasting Methods ◽  
The Neural Network

In the context of this article, a method for detecting threats based on their forecasting and development in complex distributed systems is proposed. Initially, the relevance of the research topic is substantiated from the point of view of the prospective use of various methods in the framework of threat management and their forecasting in complex distributed systems. Based on the analysis of these methods, a proprietary forecasting method based on the second generation recurrent neural network (RNN) was proposed. The mathematical formulation of the problem is presented, as well as the structure of this neural network and its mathematical model of self-learning, which allows achieving more accurate (with less error) results in the framework of threat prediction (in this case, the level of water rise at gauging stations) in complex distributed systems. An analysis was also made of the effectiveness of the existing and proposed forecasting methods, which showed the stability of the neural network in relation to other forecasting methods: the error of the neural network is 3-20% of actual (real) water levels; the least squares method reaches up to 34.5%, the numerical method in a generalized form - up to 36%; linear regression model – up to 47.5%. Thus, the neural network allows a fairly stable forecast of the flood situation over several days, which allows special services to carry out flood control measures.

ANALYSIS OF HISTORICAL DATA USING ARTIFICIAL INTELLIGENCE TECHNOLOGIES FOR PREDICTING THREATS IN COMPLEX DISTRIBUTED SYSTEMS

2021 ◽  
pp. 39-45
Author(s):  
E. V. Palchevsky ◽  
O. I. Khristodulo ◽  
S. V. Pavlov ◽  
A. V. Sokolova
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Distributed Systems ◽  
Historical Data ◽  
Mathematical Formulation ◽  
Prediction Method ◽  
Point Of View ◽  
The Neural Network ◽  
Water Rise ◽  
Artificial Neural

A threat prediction method based on the mining of historical data in complex distributed systems is proposed. The relevance of the selected research topic is substantiated from the point of view of considering floods as a physical process of water rise, the level of which is measured at stationary hydrological posts. The mathematical formulation of the problem is formulated, within the framework of which an artificial neural network is implemented based on the free software library “TensorFlow”. An analysis of the effectiveness of the implemented artificial neural network was carried out, according to the results of which the weighted mean square-law deviation of the predicted water level value from the actual one when forecasting for one day at stationary hydrological posts was 0.032. Thus, the neural network allows predicting the flood situation with acceptable accuracy, which gives time for special services to carry out measures to counter this threat.

scholarly journals Intelligent Data Analysis for Forecasting Threats in Complex Distributed Systems

2020 ◽  
pp. paper79-1-paper79-12
Author(s):  
Evgeny Palchevsky ◽  
Olga Khristodulo ◽  
Sergey Pavlov ◽  
Artur Kalimgulov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Distributed Systems ◽  
Water Level ◽  
Historical Data ◽  
Mathematical Formulation ◽  
Prediction Method ◽  
Water Levels ◽  
Average Deviation ◽  
Artificial Neural

A threat prediction method based on the intellectual analysis of historical data in complex distributed systems (CDS) is proposed. The relevance of the chosen research topic in terms of considering the flood as a physical process of raising the water level, which is measured at stationary and automatic hydrological posts, is substantiated. Based on this, a mathematical formulation of the problem is formulated, within the framework of which an artificial neural network based on the freely distributed TensorFlow software library is implemented. The analysis of the effectiveness of the implemented artificial neural network was carried out, according to which the average deviation of the predicted water level when forecasting for one day at a stationary hydrological post was 3.323%. For further research on forecasting water levels, an algorithm is proposed for evaluating historical data at automatic posts, which will allow using these data to predict water levels according to the proposed method and at automatic posts. Thus, the neural network allows predicting the flood situation with acceptable accuracy, which allows special services to take measures to counter this threat.

Incorporation of Neural Network State Estimator for GPS Attitude Determination

2004 ◽  
Vol 57 (1) ◽  
pp. 117-134 ◽  
Author(s):  
Dah-Jing Jwo ◽  
Chun-Fan Pai
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Least Squares ◽  
Kalman Filtering ◽  
Attitude Determination ◽  
Least Squares Method ◽  
State Estimator ◽  
Estimation Errors ◽  
Network State ◽  
The Neural Network

The Global Positioning System (GPS) can be employed as a free attitude determination interferometer when carrier phase measurements are utilized. Conventional approaches for the baseline vectors are essentially based on the least-squares or Kalman filtering methods. The raw attitude solutions are inherently noisy if the solutions of baseline vectors are obtained based on the least-squares method. The Kalman filter attempts to minimize the error variance of the estimation errors and will provide the optimal result while it is required that the complete a priori knowledge of both the process noise and measurement noise covariance matrices are available. In this article, a neural network state estimator, which replaces the Kalman filter, will be incorporated into the attitude determination mechanism for estimating the attitude angles from the noisy raw attitude solutions. Employing the neural network estimator improves robustness compared to the Kalman filtering method when uncertainty in noise statistical knowledge exists. Simulation is conducted and a comparative evaluation based on the neural network estimator and Kalman filter is provided.

scholarly journals Neural-Network-Based Curve Fitting Using Totally Positive Rational Bases

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2197
Author(s):  
Rocio Gonzalez-Diaz ◽  
E. Mainar ◽  
Eduardo Paluzo-Hidalgo ◽  
B. Rubio
Keyword(s):  
Neural Network ◽  
Curve Fitting ◽  
Least Squares Method ◽  
Rational Bézier Curves ◽  
The Neural Network ◽  
Totally Positive ◽  
Gradient Based ◽  
Data Points ◽  
Satisfactory Approximation ◽  
And Control

This paper proposes a method for learning the process of curve fitting through a general class of totally positive rational bases. The approximation is achieved by finding suitable weights and control points to fit the given set of data points using a neural network and a training algorithm, called AdaMax algorithm, which is a first-order gradient-based stochastic optimization. The neural network presented in this paper is novel and based on a recent generalization of rational curves which inherit geometric properties and algorithms of the traditional rational Bézier curves. The neural network has been applied to different kinds of datasets and it has been compared with the traditional least-squares method to test its performance. The obtained results show that our method can generate a satisfactory approximation.

Adaptive forecast of multi-month lake level elevations

1994 ◽  
Vol 21 (5) ◽  
pp. 778-788 ◽  
Author(s):  
Saad Bennis ◽  
Gabriel J. Assaf
Keyword(s):  
Water Level ◽  
Lake Erie ◽  
Flood Control ◽  
Ordinary Least Squares ◽  
Water Levels ◽  
Control Measures ◽  
Type Model ◽  
Model Parameters ◽  
Time Step ◽  
Kalman Predictor

The early and precise prediction of the water levels in lakes is a major concern for public authorities. Such predictions describe the evolution of the water levels and are essential for appropriate flood control measures. In this paper, a new ARMAX-type model is developed to predict, months in advance, the monthly fluctuations of the water level of Lake Erie. The predictive variables used in the model are the past monthly water levels of Lakes Erie, Superior, and Michigan–Huron along with the estimated response times between water flow entries and exits. Two scenarios are compared. The first scenario is based on the ordinary least squares (OLS) technique in order to identify the parameters of the ARMAX-type model, to filter measurement and model noises, using the ARMAX Kalman predictor (AKP), and to optimize predictions. In this scenario, the model parameters remain unchanged throughout the simulation. The second scenario is based on the mutually interactive state parameter (MISP) technique in order to readjust the parameters of the model at each time step and to filter measurement and modelling noises through the Kalman predictor. In this scenario, the parameters of the model change with time. The analysis shows that the MISP–AKP framework has a slightly higher Nash coefficient than the OLS–AKP framework for the first month. In subsequent months, however, the quality of the predictions based on the OLS–AKP technique improves significantly. This observation also applies to the persistence and extrapolation coefficients as well as to the sample autocorrelation functions for the residuals of the Lake Erie water level forecast. It was therefore decided to apply the MISP–AKP technique to obtain the first prediction of the Lake Erie level, and the OLS–AKP technique to compute subsequent predictions. Key words: adaptive, forecast, Kalman's filter, lake levels, MISP algorithm, Great Lakes.

scholarly journals INTERNET USER’S BEHAVIOR FROM THE STANDPOINT OF THE NEURAL NETWORK THEORY OF SOCIETY: PREREQUISITES FOR THE META-EDUCATION CONCEPT FORMATION

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 83-90
Author(s):  
A. S. Bakirov ◽  
Y. S. Vitulyova ◽  
A. A. Zotkin ◽  
I. E. Suleimenov
Keyword(s):  
Neural Network ◽  
Information Sources ◽  
Point Of View ◽  
Collective Unconscious ◽  
Information Structures ◽  
Dual Nature ◽  
Internet Users ◽  
The Neural Network ◽  
Meta Learning ◽  
Neural Network Theory

Abstract. An analysis of the behavior of Internet users from the point of view of their preferences in the choice of information sources and the effectiveness of their impact is presented. It is shown that the modern infocommunication space has undergone qualitative changes in the most recent time, and these transformations are already having a pronounced impact on higher education, mainly through the factor of competition between information sources. It is shown that these transformations can be interpreted as the evolution of the noosphere, which is considered as a global infocommunication network, in which non-trivial transpersonal information objects are formed. Their existence leads to the fact that the human intellect has a dual nature - both individual and collective principles are present in it at the same time. The latter is responsible for such phenomena as the collective unconscious, understood in the sense of Jung. It is shown that the neural network model of the noosphere makes it possible to formulate a similar concept of "professional collective unconscious", which is responsible for professional intuition, acts of creativity, etc. In turn, the existence of the professional collective unconscious forces us to radically reconsider the content of what is called training and move to the concept of meta-learning, which, among other things, involves stimulating transitions from one level of interaction with transpersonal information structures that make up the professional collective unconscious to another.

STATISTICAL AND MACHINE LEARNING HIGH-FREQUENCY TIME SERIES FORECASTING METHODS IN AUTOMATIC MODE

2020 ◽  
pp. 3-11
Author(s):  
Sai Van Cuong ◽  
M. V. Shcherbakov
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
High Frequency ◽  
Network Models ◽  
Time Series Forecasting ◽  
Neural Network Models ◽  
Automatic Mode ◽  
Forecasting Methods ◽  
The Neural Network

The research of the problem of automatic high-frequency time series forecasting (without expert) is devoted. The efficiency of high-frequency time series forecasting using different statistical and machine learning modelsis investigated. Theclassical statistical forecasting methods are compared with neural network models based on 1000 synthetic sets of high-frequency data. The neural network models give better prediction results, however, it takes more time to compute compared to statistical approaches.

scholarly journals Towards automatic calibration of 2-D flood propagation models

2010 ◽  
Vol 14 (6) ◽  
pp. 911-924 ◽  
Author(s):  
P. Fabio ◽  
G. T. Aronica ◽  
H. Apel
Keyword(s):  
Model Calibration ◽  
Degrees Of Freedom ◽  
Flood Control ◽  
Flood Hazard ◽  
Element Model ◽  
Water Levels ◽  
Control Measures ◽  
Automatic Calibration ◽  
Calibration Data ◽  
Model Setup

Abstract. Hydraulic models for flood propagation description are an essential tool in many fields and are used, for example, for flood hazard and risk assessments, evaluation of flood control measures, etc. Nowadays there are many models of different complexity regarding the mathematical foundation and spatial dimensions available, and most of them are comparatively easy to operate due to sophisticated tools for model setup and control. However, the calibration of these models is still underdeveloped in contrast to other models like e.g. hydrological models or models used in ecosystem analysis. This has two primary reasons: first, lack of relevant data against which the models can be calibrated, because flood events are very rarely monitored due to the disturbances inflicted by them and the lack of appropriate measuring equipment in place. Second, 2-D models are computationally very demanding and therefore the use of available sophisticated automatic calibration procedures is restricted in many cases. This study takes a well documented flood event in August 2002 at the Mulde River in Germany as an example and investigates the most appropriate calibration strategy for a simplified 2-D hyperbolic finite element model. The model independent optimiser PEST, that enables automatic calibrations without changing model code, is used and the model is calibrated against over 380 surveyed maximum water levels. The application of the parallel version of the optimiser showed that (a) it is possible to use automatic calibration in combination of 2-D hydraulic model, and (b) equifinality of model parameterisation can also be caused by a too large number of degrees of freedom in the calibration data in contrast to a too simple model setup. In order to improve model calibration and reduce equifinality, a method was developed to identify calibration data, resp. model setup with likely errors that obstruct model calibration.

scholarly journals Hybrid Neural Network Approach Based Tool for the Modelling of Photovoltaic Panels

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Antonino Laudani ◽  
Gabriele Maria Lozito ◽  
Francesco Riganti Fulginei ◽  
Alessandro Salvini
Keyword(s):  
Neural Network ◽  
Hybrid Approach ◽  
Computational Cost ◽  
Point Of View ◽  
Reduced Form ◽  
Network Approach ◽  
Neural Network Approach ◽  
Hybrid Neural Network ◽  
The Neural Network ◽  
Two Parameters

A hybrid neural network approach based tool for identifying the photovoltaic one-diode model is presented. The generalization capabilities of neural networks are used together with the robustness of the reduced form of one-diode model. Indeed, from the studies performed by the authors and the works present in the literature, it was found that a direct computation of the five parameters via multiple inputs and multiple outputs neural network is a very difficult task. The reduced form consists in a series of explicit formulae for the support to the neural network that, in our case, is aimed at predicting just two parameters among the five ones identifying the model: the other three parameters are computed by reduced form. The present hybrid approach is efficient from the computational cost point of view and accurate in the estimation of the five parameters. It constitutes a complete and extremely easy tool suitable to be implemented in a microcontroller based architecture. Validations are made on about 10000 PV panels belonging to the California Energy Commission database.

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