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On the Size of Weights for Bithreshold Neurons and Networks 16

On the Size of Weights for Bithreshold Neurons and Networks

引用

16th IEEE International Conference on Computer Science and information Technologies, CSIT 2021

作者： Kotsovsky, Vladyslav Batyuk, Anatoliy Voityshyn, Volodymyr Uzhhorod National University Department of Information Management Systems and Technologies Uzhhorod Ukraine Lviv Polytechnic National University Department of Automated Control Systems Lviv Ukraine

ISBN: (纸本)9781665442572

The paper deals with the issues concerning the memory capacity sufficient to store all coefficients of a bithre-shold neurons and networks without loss of their capability to solve classification tasks. The magnitude of integer weight coefficients of bithreshold neurons and networks providing desired binary classification of the finite set in n-dimensional space is studied. We give the bounds on the size of the norm of the integer weight vector of bithreshold neurons realizing bithreshold Boolean functions. The problem of average memory capacity is also considered. We prove the quadratic (on the number of inputs) lower bounds on the capacity of the memory that is necessary to store the parameters of threshold and bithreshold neurons for almost all threshold and bithreshold functions. © 2021 IEEE.

关键词： Neurons

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Decision List-Based Representation of Bithreshold Functions 16

Decision List-Based Representation of Bithreshold Functions

引用

16th IEEE International Conference on Computer Science and information Technologies, CSIT 2021

作者： Kotsovsky, Vladyslav Batyuk, Anatoliy Department of Information Management Systems and Technologies Uzhhorod National University Uzhhorod Ukraine Department of Automated Control Systems Lviv Polytechnic National University Lviv Ukraine

ISBN: (纸本)9781665442572

The paper deals with the issues concerning the computation of Boolean bithreshold functions by decision lists. We give and justify conditions ensuring the possibility of the realization of a decision list by a bithreshold neural unit. It is shown that a decision list comprising monomials and special 2-term-2-DNF formulas represents a bithreshold Boolean function. The simple and quick learning algorithm is proposed, which yields the weights and the thresholds of the sought bithreshold neuron. We also give the time complexity of this algorithm and estimate the upper bound on the size of the weight vector and thresholds. © 2021 IEEE.

关键词： Learning algorithms

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Research of methods for predicting performance indicators on the example of an oil field 7

Research of methods for predicting performance indicators on...

引用

7th IEEE International Conference on information Technology and Nanotechnology, ITNT 2021

作者： Mokshin, Vladimir Kurbanov, Babakhan Tukhbatullin, Timur Khanova, Zulfiya Kazan National Research University Named after A.N. Tupolev - KAI Department of Automated Information Processing Systems and Management Kazan Russia

ISBN: (纸本)9781665432177

In this paper, we consider a model for researching oil wells using neural networks and forecasting in the Matlab environment. The Levenberg-Marquardt algorithm and the gradient descent method are considered as training methods. The graphs illustrating the comparison of the methods and the results of the work of the algorithms under consideration are presented. The quality of the model is assessed according to the Fisher criterion and the multiple determination coefficient for three hundred and four hundred output values. Oil well survey data were used for the analysis © 2021 IEEE.

关键词： Forecasting

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Model for Determining the Optimal Structure of Hierarchical Asymmetric Branched Computer Networks 32

Model for Determining the Optimal Structure of Hierarchical ...

引用

32nd IEEE International Conference on Microelectronics, MIEL 2021

作者： Teslyuk, V. Pavliuk, O. Kazymyra, I. Institute of Computer Science and Information Technology Lviv Polytechnic National University Department of Automated Control Systems Lviv79013 Ukraine

ISBN: (纸本)9781665445283

A mathematical model has been elaborated to determine the optimal structure of multilevel hierarchical asymmetric branched computer networks and the appropriate software has been developed. In the presented case study on the example of a star topology with a selected main element and given characteristics of the elements' reliability, the optimal structure of the connection of workstations to the hubs was selected. © 2021 IEEE.

关键词： Structural optimization

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Prediction of the Magnitude of Lightning Impulse Interference in an Electronic Device Using an Artificial Neural Network

Prediction of the Magnitude of Lightning Impulse Interferenc...

引用

International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Zinnur Gizatullin Ramis Mubarakov Department for Automated Systems for Information Processing and Control Department for Computer-Aided Design Systems Kazan National Research Technical University named after A.N. Tupolev-KAI Kazan Russian Federation Department for Computer-Aided Design Systems Kazan National Research Technical University named after A.N. Tupolev-KAI Kazan Russian Federation

ISBN: (数字)9798350395013

ISBN: (纸本)9798350395020

Reliable operation of modern electronic devices is the basis for their effective use. Electromagnetic interference, such as lightning, creates the risk of temporary or permanent disruption of the functioning of device elements. Therefore, it is necessary to take into account the possible consequences of such interference on the basis of prediction even at the design stage of electronic devices. The paper a technique for predicting the magnitude of lightning impulse interference in electronic devices using an artificial neural network is proposed. The advantage of this approach over simulation modeling is that rather complex three-dimensional models and long computation times are not needed. A stand was developed and obtained by measuring the amount of lightning interference in an electronic device. The features of the task of training an artificial neural network based on experimental data are considered. The architecture and parameters of the artificial neural network have been selected. An artificial neural network was trained based on experimental data. The root mean square error between the predicted data and the average impulse interference voltage values on the training data does not exceed acceptable values. The results of predicting the magnitude of lightning interference in an electronic device are presented. If we consider further prospects for using an artificial neural network to predict electromagnetic interference, we can note the relevance of the problem of the impact of electrostatic discharge, intentional short pulse and etc.

关键词： Training Solid modeling Magnetic field measurement Computational modeling Electromagnetic interference Lightning Training data

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Subsystem to Predict Quality Indicators of Iron Ore processing on Basis of Hybrid Deep Neural Network Models for Each Particular Production Unit 3

Subsystem to Predict Quality Indicators of Iron Ore Processi...

引用

3rd International Conference on control systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2021

作者： Poleshchenko, D.A. Tsygankov, Y.A. Kurgalin, S.D. Automated and Information Control Systems Department NUST MISIS Stary Oskol Russia Voronezh State University Computer Science Department Voronezh Russia

ISBN: (纸本)9781665439817

The mining and metallurgical sector is currently the most energy-intensive in Russia's industrial structure. The large volumes of raw materials being processed, the production of products and the high demand for them on the world market make it urgent to increase the efficiency of all stages of production. In the absence of changes in production technology and significant capital costs associated with the replacement of existing equipment, only changes in the algorithm and software of the control systems can be made. The process of iron ore concentrate enrichment at the ore processing factory is a discrete and continuous sequence of production operations. Their implementation takes place in conditions of variability in the physical and mechanical properties of the incoming raw material, caused by the variability of the mineralogical composition, by the drift of the parameters of the equipment due to the operational wear of parts and other features, significantly complicating automated control. Quality control of the enrichment stage-percentage of iron in a concentrate-is not implemented in an automated mode and is performed only by laboratory analysis for which taking samples is completed discretely, with an interval significantly exceeding the duration of the process. This greatly complicates the management of technological stages, as they need to be implemented in compliance with regulations, taking into account external factors, many of which are not readily available or measured at all. In addition, the requirements for achieving target iron content and overall performance are to be met. © 2021 IEEE.

关键词： Deep neural networks

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Testing automated Complex for Assessing Agrotechnical Parameters of Agricultural Crops Based on Photographs Taken by an Unmanned Aerial Vehicle (UAV) in a Mock-Up of a Real Field

Testing Automated Complex for Assessing Agrotechnical Parame...

引用

Digital Signal processing and its Applications (DSPA), International Conference on

作者： Dmitriy Poleshchenko Ilia Mikhailov Vladislav Petrov Vladislav Stankevich Automated and information control systems department Stary Oskol Technological Institute n.a. A.A. Ugarov (branch) National University of Science and Technology MISIS Stary Oskol Russia

The work is devoted to approbation of methods for assessing agrotechnical parameters of agricultural crops from UAV photos using neural networks on the developed laboratory bench with a real field layout. The laboratory bench is used to determine the number of corn mock-ups, weeds and the degree of weediness of a field plot based on YOLOv5 and YOLOv8 architectures. This laboratory bench allows to conduct research under controlled conditions and improve the efficiency of the complex.

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Adaptive Machine Learning Methods in Oil Well Research

Adaptive Machine Learning Methods in Oil Well Research

引用

International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Vladimir Mokshin Ivan Kychkin Ainur Minigaliev Department of Automated Information Processing Systems and Management Kazan National Research Technical University Named After A. N. Tupolev - KAI Kazan Russia

This article discusses the oil well exploration model using neural networks and prediction in Python. The problem of identifying significant features is considered. Decision tree algorithms, Sequential Forward Selection (SFS), Sequential Backward Selection (SBS) and Exhaustive Feature Selection (EFS) are considered as selection methods. Graphs are presented illustrating comparisons of methods and results of algorithms, and an assessment of the quality of methods is also considered. The paper considers several types of neural networks for building an oil well survey model based on learning methods: the Levenberg-Marquardt method, the Bayesian regularization method, the error backpropagation method. The results of evaluating effective performance indicators from the above teaching methods and methods for highlighting significant features are shown.

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Researching Machine Learning Methods for Preventing Cardiovascular Diseases

Researching Machine Learning Methods for Preventing Cardiova...

引用

information Technology and Nanotechnology (ITNT), International Conference on

作者： Daria Grigorieva Alina Faskhutdinova Bulat Garafutdinov Vladimir Mokshin Department of Automated Information Processing Systems and Management Kazan National Research Technical University Named After A. N. Tupolev – KAI Kazan Russia

In this article, a review of existing methods for developing a model for the prevention of cardiovascular diseases was carried out, their advantages and disadvantages were identified. Mortality and morbidity from heart disease has been leading in recent decades throughout the world. The use of various machine learning algorithms, including deep learning algorithms, significantly improves the accuracy of predicting cardiovascular risks of trained models. Using the data obtained, we created a model with which we can identify a group of people who are more at risk of heart disease.

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Development of the Predictive control System for Ethylbenzene Dehydration 3

Development of the Predictive Control System for Ethylbenzen...

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3rd International Conference on control systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2021

作者： Popov, Alexey Tikhomirov, Sergey Neizvestny, Oleg Podvalny, Semyon Voronezh State University of Engineering Technologies Department of Information and Control Systems Voronezh Russia Voronezh State Technical University Department of Automated and Computing Systems Voronezh Russia

ISBN: (纸本)9781665439817

the article describes the application of the predictive functional control method in the synthesis of an automated control system for the ethylbenzene dehydrogenation. Based on the results of the control object system analysis, the choice of the control method has been justified, a review and assessment of the possibility of using various types of control devices have been carried out. Within the framework of the model-based design concept, a control system has been synthesized that implements predictive functional control (PFC) of the styrene concentration at the output from the first stage of the reactor block. Structural synthesis has been performed and an algorithm for the functioning of the control system has been developed. A complex of dynamic models describing heat exchange processes and physicochemical transformations occurring during the dehydrogenation of ethylbenzene has been used as a structural element for constructing a predicted trajectory of changes in the control and controlled parameters. The calculation of the control action has been carried out taking into account the inertia of the process equipment, the state parameters of the reactor's catalytic layer, the values of energy flows and concentrations of the components. A comparative analysis of the results of calculating transient processes for the main channel of the control system, obtained using a PID structure controller and a control device that implements the PFC control method, has been presented. It was found that the use of the proposed algorithm and control system allows for the on-line computation of control actions and timely compensation for the decrease in the activity of the reactor's catalytic layer, which, in turn, prevents the decrease in the yield of the target product below the preset value, and also increases the energy efficiency and productivity of the reactor plant. © 2021 IEEE.

关键词： Styrene

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