Application of intelligent technologies for modeling of controlled switching systems

Science Intensive Technologies ◽

10.18127/j19998465-202104-04 ◽

2021 ◽

Author(s):

D.Y. Openkin ◽

S.V. Chernomordov

Keyword(s):

Neural Network ◽

Numerical Optimization ◽

Pid Controller ◽

Optimization Methods ◽

Practical Significance ◽

Transport Systems ◽

Neural Network Modeling ◽

Switching Systems ◽

Controlled Switching ◽

Algorithmic Software

The development of instrumental and methodological support for modeling nonlinear control systems with switching is an urgent problem. Various modifications of numerical optimization methods and artificial intelligence technologies are used to solve this problem. The purpose of this article is to develop algorithmic software for modeling controlled switching systems based on the use of intelligent technologies and numerical optimization methods. Algorithmic software for the synthesis of feedback controls using a PID controller is developed. Intelligent technologies for modeling controlled switching systems are characterized. An algorithm using global parametric optimization methods is proposed for tuning the PID controller. Models of nonlinear dynamic switching systems are studied. An algorithm for finding optimal trajectories based on neural network automata is proposed. The basis for further research is developed, in which it is planned to create a software implementation of the switching algorithm and the neural network algorithm. The practical significance of the results is that the developed algorithmic software will allow analyzing the influence of various parameters on the quality and speed of functioning of intelligent control switching systems. The obtained results can be used in various problems of modeling and global optimization of controlled systems: technical systems with switching modes of operation, transport systems, as well as in problems of neural network modeling and machine learning.

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Application of various optimization methods in calculating the error of a neural network for diagnosing parasitological diseases of the gastrointestinal tract

Biomedical Radioelectronics ◽

10.18127/j15604136-202101-04 ◽

2021 ◽

Author(s):

N.T. Abdullaev ◽

U.N. Musevi ◽

K.S. Pashaeva

Keyword(s):

Neural Network ◽

Neural Networks ◽

Gastrointestinal Tract ◽

Optimization Methods ◽

Optimization Method ◽

Medical Diagnostics ◽

The Body ◽

Conjugate Gradients ◽

Practical Significance ◽

Parasitic Diseases

Formulation of the problem. This work is devoted to the use of artificial neural networks for diagnosing the functional state of the gastrointestinal tract caused by the influence of parasites in the body. For the experiment, 24 symptoms were selected, the number of which can be increased, and 9 most common diseases. The coincidence of neural network diagnostics with classical medical diagnostics for a specific disease is shown. The purpose of the work is to compare the neural networks in terms of their performance after describing the methods of preprocessing, isolating symptoms and classifying parasitic diseases of the gastrointestinal tract. Computer implementation of the experiment was carried out in the NeuroPro 0.25 software environment and optimization methods were chosen for training the network: "gradient descent" modified by Par Tan, "conjugate gradients", BFGS. Results. The results of forecasting using a multilayer perceptron using the above optimization methods are presented. To compare optimization methods, we used the values of the minimum and maximum network errors. Comparison of optimization methods using network errors makes it possible to draw the correct conclusion that for the task at hand, the best results were obtained when using the "conjugate gradients" optimization method. Practical significance. The proposed approach facilitates the work of the experimenter-doctor in choosing the optimization method when working with neural networks for the problem of diagnosing parasitic diseases of the gastrointestinal tract from the point of view of assessing the network error.

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An approach to assessing the technical condition of elements and nodes of transport systems using neural network modeling methods and digital twin technology

Neurocomputers ◽

10.18127/j19998554-202105-01 ◽

2021 ◽

Author(s):

V.V. Belousov ◽

O.V. Druzhinina ◽

E.R. Korepanov ◽

I.V. Makarenkova ◽

V.V. Maksimova

Keyword(s):

Neural Network ◽

Intelligent Systems ◽

Technical Condition ◽

The State ◽

Transport Infrastructure ◽

Transport Systems ◽

Neural Network Modeling ◽

Technical Diagnostics ◽

Digital Twin ◽

Digital Twins

The development of intelligent methods and the development of tools for solving research problems of modeling and diagnosing the state of technical systems are relevant areas related to the introduction of digital technologies. Such problems include the problems of preparing correct data arrays for diagnosing and predicting the state of elements and nodes of transport systems. The purpose of the paper is to develop methods for preparing and analyzing data for modeling digital twins on the example of studying the temperature regime of the functioning of a railway car's axle box node, as well as choosing the architecture of neural networks for solving problems of assessing the technical condition of the axle box nodes. The features of the digital twin technology are characterized and the directions of its use for modeling and diagnostics of railway transport systems are considered. As part of the development of an approach to solving the problem of diagnosing the condition of the axle box units of railway cars, analytical dependences and generalized heat transfer equations related to changes in temperature regimes are considered. Examples of the presentation of such data on temperature series, which contain values for deviations from the normal functioning of the elements of the axle box node, are given. The choice of the neural network architecture adapted for solving the problems of estimating and predicting the temperature values of the axle box unit of the car was made. Options for preparing test data for a neural network model have been developed. The results can be used in the problems of creating algorithmic and software for preparing correct arrays of input data for technical diagnostics, in the problems of synthesis and analysis of models of intelligent systems, in various machine learning problems. The considered approach to modeling is aimed at developing methods for assessing and predicting the state of transport infrastructure elements and can be used in the development of intelligent transport systems and for improving the technology of digital twins.

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Neural Network Optimization Algorithms for Controlled Switching Systems

Advances in Intelligent Systems and Computing - Artificial Intelligence and Bioinspired Computational Methods ◽

10.1007/978-3-030-51971-1_39 ◽

2020 ◽

pp. 470-483

Author(s):

Olga V. Druzhinina ◽

Olga N. Masina ◽

Alexey A. Petrov ◽

Evgeny V. Lisovsky ◽

Maria A. Lyudagovskaya

Keyword(s):

Neural Network ◽

Network Optimization ◽

Optimization Algorithms ◽

Switching Systems ◽

Neural Network Optimization ◽

Controlled Switching

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The ACA-BP Algorithm Based Controller of PID Neural Network and its Simulation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.239-240.1377 ◽

2012 ◽

Vol 239-240 ◽

pp. 1377-1381

Author(s):

Fang Ding ◽

Dong Jian Huang

Keyword(s):

Neural Network ◽

Pid Controller ◽

Ant Colony Algorithm ◽

Setting Time ◽

Bp Algorithm ◽

Practical Significance ◽

Optimal Parameters ◽

Neuron Network ◽

Network Weight ◽

Pid Neural Network

Aiming at the shortages of traditional PID controller, and problems of hardly setting the controller parameters, and the setting time is long, this paragraph gives a design of ACA-BP arithmetic based controller of PID Neural Network. First, uses the Ant Colony algorithm to thickly select the PID neuron network weight parameter. Then adjust parameters online by PID Neural Network and BP algorithm. Finally, we can obtain optimal parameters. The simulation result shows, compared with traditional PID controller, this controller has greatly improved its control performances. This would have some theoretical and practical significance.

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