scholarly journals Neural Network Algorithm for Stabilizing Mechanized Systems

2022 ◽  
Vol 17 ◽  
pp. 15-20
Author(s):  
Elena G. Shmakova ◽  
Olga A. Filoretova ◽  
Olga M. Nikolaeva ◽  
Denis P. Vasilkin
Keyword(s):  
Neural Network ◽  
Experimental Model ◽  
High Speed ◽  
Experimental Studies ◽  
Small Diameter ◽  
Hopfield Neural Network ◽  
Neural Network Approach ◽  
Rolling System ◽  
Proportional Integral ◽  
Mechanized System

The article describes an experimental model of stabilization of a mechanized system. The following are shown: a skate; an element of the program code; an algorithm for stabilizing a proportional-integral-differential controller (PID). The experimental model uses the calculation and adjustment of the regulator according to the Ziegler-Nichols method. For the case of applying the neural network approach to the search for equilibrium, the Hopfield neural network is used. The technology of calculating the balancing of the values of the coefficients: proportional, integral, differential components are described. The design of the rolling system is described. The experimental model is designed to identify the balancing range of the rolling system of small-diameter balls. The experimental module balances the ball at a distance of 4.5 to 7 cm (SW-range). The shortcomings of the experimental model of stabilization of the mechanized system are revealed. The analysis of experimental studies of spacecraft stabilization is carried out. It is determined that it is advisable to use the mathematical tools of the sixth-order Butterworth polynomial in the training of a neural network. Complex neural network calculations make it possible to calculate the stabilization coefficients of the spacecraft when the coordinate system does not coincide with the axes of inertia. An overview of the authors ' research on the use of intelligent quality control systems for the production of medicines is given. An overview of neural network solutions for stabilizing the turning angle of high-speed cars is given. The expediency of selecting the stabilization coefficients of a proportional-integral-differential regulator by a trained neural network for various rolling ranges is proved.

Predicting the Performance of High-Speed Railway Bridge Using Regression Neural Network Approach

2017 ◽  
Vol 873 ◽  
pp. 220-224 ◽  
Author(s):  
Young Chan Kim ◽  
Mosbeh R. Kaloop ◽  
Jong Wan Hu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
High Speed ◽  
Short Term ◽  
Good Tool ◽  
Neural Network Approach ◽  
High Speed Railway ◽  
Railway Bridges ◽  
Bridge Girder ◽  
Artificial Neural Network Ann

The performance prediction of High-speed railway bridges (HSRB) is vital to detect the behavior of bridges under different train’s speeds. This study aims to design a prediction model using the artificial neural network (ANN) to assess the performance of Yonjung high-speed bridge. A short-term health monitoring system is used to collect the behavior of bridge with different high-speed train’s speeds. The statistical analysis is utilized to evaluate the bridge under speeds 165 to 403 Km/h. The evaluation of bridge and prediction model showing that the bridge is safe, and the ANN is shown a good tool can be used to estimate a prediction model for the displacement of bridge girder.

scholarly journals A Neural Network Approach to Pricing in the Precious Metals Market

2019 ◽  
Author(s):  
V. E. Bilozorov ◽  
A. S. Ivlev
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Experimental Studies ◽  
Precious Metals ◽  
Mathematical Statistics ◽  
Coefficient Of Determination ◽  
Future Price ◽  
Neural Network Approach ◽  
Modern Development ◽  
Artificial Neural

The modern development of the science of artificial neural networks (ANN) has allowed to use their nature and properties in various applied fields of science. One of the most important applications of ANN is the modeling of prices in the precious metals market. Just like in any other market, based on the prediction of current prices, because the ability of ANN to learn like a true biological neural network, relying on the input with subsequent testing of the output, provides a significant advantage in the prediction tasks compared to the classical mathematical algorithms. Predicting the price of precious metals with relatively high precision and low error is in great demand among all individuals and legal entities that carry out transactions which are directly related to the purchase and sale of these precious metals, since accurate knowledge of the future price of a particular metal will bring maximum benefits of these operations. Numerous methods have been developed [2-4] for the use of neural networks in the modeling of price forecasts, which make the prediction of the rate of exchange for a particular currency (rather objective). The applied methods make the prediction using the classical perceptron along with astrological cyclic indices [2], recursive neural networks [3], and/or using elements of mathematical statistics (for example, use of U-statistic and the coefficient of determination ) [4]. The goal of this paper is the attempt to usethe ANN in the forecasting problem that allows predicting the price of precious metals in the near future, based on an algorithm that makes predictions by learning based on an array of input data and does not depend on the said elements of mathematical statistics. The paper presents a new method for using an artificial neural network in forecasting problems. Experimental studies of this method were carried out on the basis of the precious metals pricing rate on the Ukrainian Interbank Exchange. The corresponding conclusions are made regarding the effectiveness of the method and the possibilities for its further improvement based on the results of these studies. It is expected that such an algorithm will give a prediction as close as possible to the real value.

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