Application of Artificial Neural Network Technology for Prediction of Sunflower Harvest Losses

Application of Artificial Neural Network Technology for Prediction of Sunflower Harvest Losses

Gespeichert in:

Bibliographische Detailangaben
Datum:	2022
Hauptverfasser:	O. Zozulya, V. Domrachev, V. Tretynyk
Format:	Artikel
Sprache:	English
Veröffentlicht:	2022
Schriftenreihe:	Cybernetics and Computer Technologies
Online Zugang:	http://jnas.nbuv.gov.ua/article/UJRN-0001359015
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!
Назва журналу:	Library portal of National Academy of Sciences of Ukraine \| LibNAS

Institution

Library portal of National Academy of Sciences of Ukraine | LibNAS

Ähnliche Einträge

APPLICATION OF AN ARTIFICIAL NEURAL NETWORK FOR PREDICTING THE THERMAL STATE OF AN AIR-SOIL HEAT EXCHANGER
von: Basok , B., et al.
Veröffentlicht: (2025)

Analysis of Artificial Neural Networks Training Models for Airfare Price Prediction
von: Ye. O. Kuptsova, et al.
Veröffentlicht: (2020)

Brain and Artificial Neural Networks
von: V. V. Storozh
Veröffentlicht: (2013)

Application of neural networks to prediction of company future development
von: L. Dvořáková, et al.
Veröffentlicht: (2015)

Application of neural networks to prediction of company future development
von: Dvořáková, L., et al.
Veröffentlicht: (2015)

Predictive Performance of Artificial Neural Network and Multiple Linear Regression Models in Predicting Adhesive Bonding Strength of Wood
von: Bardak, S., et al.
Veröffentlicht: (2016)

Application of neural network technologies in NDT systems
von: V. S. Yeremenko, et al.
Veröffentlicht: (2012)

Usage of artificial neural networks in the energy sector
von: D. V. Lazarenko
Veröffentlicht: (2016)

Usage of artificial neural networks in the energy sector
von: Lazarenko, D.V.
Veröffentlicht: (2016)

Artificial neural networks using for solving of the tribological problems
von: V. M. Uvarov, et al.
Veröffentlicht: (2019)

Prediction of the future development of construction companies by means of artificial neural networks on the basis of data from the Czech Republic
von: M. Vochozka, et al.
Veröffentlicht: (2015)

Prediction of the future development of construction companies by means of artificial neural networks on the basis of data from the Czech Republic
von: Vochozka, M., et al.
Veröffentlicht: (2015)

Artificial neural networks: future of use in law enforcement
von: M. I. Maietnyi
Veröffentlicht: (2021)

Artificial Neural Networks for Modeling of Crisis Management of National Economy
von: Ya. Savka
Veröffentlicht: (2020)

Using the Artificial Neural Networks for Forecasting the Risk of Bankruptcy of Banks
von: Ye. Markov
Veröffentlicht: (2018)

Using Artificial Neural Networks to Simulate the Financial Stability of Enterprise
von: L. N. Debunov
Veröffentlicht: (2017)

NEURAL NETWORK MODELING IN PROBLEMS OF PREDICTION MODES OF ELECTRICAL GRIDS
von: Moroz, A. N., et al.
Veröffentlicht: (2016)

New application of artificial neural network-based direct power control for permanent magnet synchronous generator
von: Akkouchi, K., et al.
Veröffentlicht: (2021)

Combined Autoregressive-Neural Network Method for Predicting Time Series
von: G. A. Kravtsov, et al.
Veröffentlicht: (2020)

An Artificial Neural Networks Modeling Environment for Solving a Clustering Task
von: Yu. V. Moskalenko
Veröffentlicht: (2019)

The use of Artificial Neural Networks for Process Control Hardware and Processing Complex
von: Ju. Zarubin, et al.
Veröffentlicht: (2013)

Prediction of side effects of polypragmation using a graphical neural network
von: V. P. Kuznyak, et al.
Veröffentlicht: (2024)

Peculiarities of development of the artificial neural network used in the hybrid expert system
von: S. N. Konovalov, et al.
Veröffentlicht: (2018)

Application of neural networks in OLAP-systems
von: Nabibayeva, G.Ch.
Veröffentlicht: (2024)

Prediction of Audience Reaction on Text-Visual Content Using Neural Networks
von: A. V. Anisimov, et al.
Veröffentlicht: (2021)

Predictive control of nonlinear objects using evolving feedforward neural networks
von: O. G. Rudenko, et al.
Veröffentlicht: (2015)

A prediction of the frequency of non-periodic signals based on convolutional neural networks
von: S. O. Subbotin, et al.
Veröffentlicht: (2018)

A prediction of the frequency of non-periodic signals based on convolutional neural networks
von: Subbotin, S. A., et al.
Veröffentlicht: (2018)

Application of the volume learning algorithm artificial neural networks for recognition of the type of interaction between neurons from their cross-correlation histograms
von: Kovalishyn, V.V., et al.
Veröffentlicht: (2005)

Study of stochastic gradient methods for optimization of algorithms of learning artificial neural networks
von: T. A. Samoljuk
Veröffentlicht: (2017)

Neural Network Technologies in the Problem of Handwriting Recognition
von: A. D. Goltsev, et al.
Veröffentlicht: (2018)

Neural network application to pseudorandom sequence generation simulation
von: Lesyk, V.O., et al.
Veröffentlicht: (2024)

Neural network-based seismic resolution enhancement technology
von: O. V. Noskov
Veröffentlicht: (2024)

Adaptive Quasi-Optimal Control in Pulse Convertors with Artificial Neural Network Model of Power Part
von: V. P. Vojtenko
Veröffentlicht: (2016)

Application of neural networks in the classification of medical images textures
von: R. Dzierїak, et al.
Veröffentlicht: (2018)

Application of neural networks in the classification of medical images textures
von: Dzierżak, R., et al.
Veröffentlicht: (2018)

Fault diagnosis in a five-level multilevel inverter using an artificial neural network approach
von: Parimalasundar, E., et al.
Veröffentlicht: (2023)

Results of application of modular artificial neural networks for intelligent data analysis (data mining) and forecasting processes in the field of ecology and environment protection
von: R. M. Trokhymchuk
Veröffentlicht: (2016)

Results of application of modular artificial neural networks for intelligent data analysis (data mining) and forecasting processes in the field of ecology and environment protection
von: Trokhymchuk, R.M.
Veröffentlicht: (2016)

Methods of artificial intelligence for acoustic emission diagnostics of fracture stages (A review). P. 2: Artificial neural network and deep learning
von: O. M. Stankevych, et al.
Veröffentlicht: (2024)