A robotic complex control method based on deep reinforcement learning of recurrent neural networks for automatic harvesting of greenhouse crops

A robotic complex control method based on deep reinforcement learning of recurrent neural networks for automatic harvesting of greenhouse crops

The modern development of technology determines the feasibility of the transition in agriculture from manual labor to automatic production. One of the promising areas is the automation of growing vegetable crops in greenhouse complexes. Necessary factors for intensive plant growth and unfavorable fo...

Spremljeno u:

Bibliografski detalji
Glavni autori:	Petrenko, V. I., Петренко, В. И., Tebueva, F. B., Тебуева, Ф. Б., Gurchinsky, M. M., Гурчинский, М. М., Antonov, V. O., Антонов, В. О.
Format:	Статья
Jezik:	English
Izdano:	ATLANTIS PRESS 2021
Teme:	Automation and robotics Decision- making Deep reinforcement learning Recurrent neural networks Recurrent Q-networks
Online pristup:	https://dspace.ncfu.ru/handle/20.500.12258/18124
Oznake:	Dodaj oznaku Bez oznaka, Budi prvi tko označuje ovaj zapis!

Similar Items

Feature importance evaluation method for multi-agent deep reinforcement learning in advanced robotics task allocation
od: Ryabtsev, S. S., i dr.
Izdano: (2022)

Multi-agent deep reinforcement learning concept for mobile cyber-physical systems control
od: Petrenko, V. I., i dr.
Izdano: (2021)

Method for reconfiguring the kinematic structure of a mechatronic-modular robot in non-deterministic conditions
od: Petrenko, V. I., i dr.
Izdano: (2021)

Using virtual data for training deep model for hand gesture recognition
od: Nikolaev, E. I., i dr.
Izdano: (2018)

Soil columns with reinforcement
od: Steshenko, D. M., i dr.
Izdano: (2018)

On the Unassociated Matrices Number of the n Order and a Given Determinant
od: Pachev, U. M., i dr.
Izdano: (2023)

Machine learning algorithm for anthropomorphic manipulator control system
od: Petrenko, V. I., i dr.
Izdano: (2021)

Study of dispersed-reinforced expanding plugging materials to improve the quality of well cementing
od: Minchenko, Y. S., i dr.
Izdano: (2021)

Development of prognostic neural network models based on non-positional coding for automatic control systems
od: Tikhonov, E. E., i dr.
Izdano: (2019)

A method for predicting the evolvement of an emergency zone based on artificial neural networks
od: Petrenko, V. I., i dr.
Izdano: (2021)

Style transfer for CNC machine input data preprocessing
od: Nikolaev, E. I., i dr.
Izdano: (2019)

On the influence of cross-section and reinforcement of reinforced concrete constructions on the concentration of coarse aggregate in concrete with frame structure
od: Kolleganov, A. V., i dr.
Izdano: (2020)

Neural network technologies in economics study aid
od: Kovalenko, A. V.

Development of a model for detecting security incidents in event flows from various components in a network of telecommunication service providers
od: Lapina, M. A., i dr.
Izdano: (2020)

Optimization of computational complexity of an artificial neural network
od: Vershkov, N. A., i dr.
Izdano: (2021)

Simultaneous use of imitation learning and reinforcement learning in artificial intelligence development for video games
od: Karavaev, V. Y., i dr.
Izdano: (2019)

Development of a convolution algorithm for the content of a Web resource for processing by a neural network
od: Gorshkov, S. N., i dr.
Izdano: (2020)

Hamming neural network in discrete form
od: Shaposhnikov, A. V., i dr.
Izdano: (2022)

Neural network approach to prediction of the liquid petroleum products viscosity
od: Koldaev, A. I., i dr.
Izdano: (2021)

Analytical review of the methods of dynamic load balancing under conditions of uncertainty in the execution time of tasks
od: Shiriaev, E. M., i dr.
Izdano: (2021)

Energy-balanced distribution of radio modules with various technical states among positions of nodes in wireless sensor networks
od: Fedorenko, V. V., i dr.
Izdano: (2021)

Automatic system for evaluating the quality of the display of goods in a smart store based on cascading neural networks
od: Talalaeva, J., i dr.
Izdano: (2020)

Neural network decoder of automatic process control system
od: Koldaev, A. I., i dr.
Izdano: (2020)

Applying a Generative Adversarial Approach to Build an Intelligent Control System for Robotic Systems
od: Nikolaev, E. I., i dr.
Izdano: (2023)

Using neural network technologies to assess the quality characteristics of food
od: Sadovoy, V. V., i dr.
Izdano: (2021)

Neural network analysis for image classification
od: Vershkov, N. A., i dr.
Izdano: (2022)

On the Possibility of Analytical Calculation of the 380 V Distribution Networks Parameters by Automated Energy Consumption Metering Systems
od: Danilov, M. I., i dr.
Izdano: (2022)

Resource-saving technology of wastewater treatment of factories of reinforced concrete
od: Shchitova, T. V., i dr.
Izdano: (2019)

An intelligent system for content generation
od: Nikolaev, E. I., i dr.
Izdano: (2018)

An intelligent system for content generation
od: Nikolaev, E. I., i dr.
Izdano: (2018)

Optimization of Artificial Neural Networks using Wavelet Transforms
od: Vershkov, N. A., i dr.
Izdano: (2023)

Method for Convolutional Neural Network Hardware Implementation Based on a Residue Number System
od: Valueva, M. V., i dr.
Izdano: (2023)

The use of GIS technologies in the management of the urban transportation system
od: Bondarenko, N. G., i dr.
Izdano: (2019)

Identification of the parameters of distribution networks by synchronized current and voltage measurements
od: Zelensky, E. G., i dr.
Izdano: (2018)

Adaptive system monitoring of the technical condition technological objects based on wireless sensor networks
od: Fedorenko, V. V., i dr.
Izdano: (2020)

Neural Networks in Production
od: Rudakova, T. A., i dr.
Izdano: (2024)

Residue number system-based solution for reducing the hardware cost of a convolutional neural network
od: Chervyakov, N. I., i dr.
Izdano: (2020)

Hardware and software implementation of neural network control of power systems based on the system of residual classes
od: Tikhonov, E. E., i dr.
Izdano: (2020)

Application CUDA for optimization ANN in forecasting electricity on industrial enterprise
od: Taranov, R. V., i dr.
Izdano: (2018)

Transverse-layer partitioning of artificial neural networks for image classification
od: Vershkov, N. A., i dr.
Izdano: (2024)