Neural networks fundamentals in mobile robot control systems Учебное пособие

Neural networks fundamentals in mobile robot control systems Учебное пособие

Включает полное и систематизированное изложение материала по учебной программе курса «Интеллектуальные системы управления роботами». Адресован студентам обучающимся по программам бакалавриата и магистратуры по специальности «Мехатроника и робототехника» Института радиотехники и систем управления Южн...

Szczegółowa specyfikacja

Zapisane w:

Opis bibliograficzny
1. autor:	Медведев М.Ю
Format:	Учебное пособие
Dostęp online:	https://znanium.com/catalog/document?id=375025 https://znanium.com/cover/1308/1308419.jpg
Etykiety:	Dodaj etykietę Nie ma etykietki, Dołącz pierwszą etykiete!

Podobne zapisy

Neural networks fundamentals in mobile robot control systems учебное пособие
od: Медведев, М. Ю.

Protected group control system for mobile robots
od: Lapina, M. A., i wsp.
Wydane: (2021)

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

Implementation of CMAC neural network control systems in residue number system
od: Koldaev, A. I., i wsp.
Wydane: (2020)

Optimization of neural network computation with use of residual number system for tasks of design of neural network systems of automatic control
od: Tikhonov, E. E., i wsp.
Wydane: (2019)

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

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

A robotic complex control method based on deep reinforcement learning of recurrent neural networks for automatic harvesting of greenhouse crops
od: Petrenko, V. I., i wsp.
Wydane: (2021)

Neural Networks in Production
od: Rudakova, T. A., i wsp.
Wydane: (2024)

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

Neural network method for base extension in residue number system
od: Babenko, M. G., i wsp.
Wydane: (2020)

Host-based method and system for detecting anomalies in network traffic for a robotic system
od: Lapina, M. A., i wsp.
Wydane: (2019)

Development of telecommunication system units in parallel neural network basis
od: Smirnov, A. A., i wsp.
Wydane: (2020)

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

Modeling of Distributed Control System for Network of Mineral Water Wells
od: Pershin, I. M., i wsp.
Wydane: (2023)

Increasing of convolutional neural network performance using residue number system
od: Chervyakov, N. I., i wsp.
Wydane: (2018)

Mobile Laboratory for Identification of Radial Distribution Network Topology
od: Zelensky, E. G., i wsp.
Wydane: (2023)

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

Modeling hyperchaotic datasets for neural networks
od: Shiriaev, E. M., i wsp.
Wydane: (2022)

The wave model of artificial neural network
od: Vershkov, N. A., i wsp.
Wydane: (2020)

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

Analysis of Neural Networks for Image Classification
od: Vershkov, N. A., i wsp.
Wydane: (2023)

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

Adaptive finite ring neural network
od: Evdokimov, A. A., i wsp.
Wydane: (2019)

Semantic Segmentation System of Pigmented Skin Lesions Based on Convolutional Neural Networks
od: Fedorenko, V. V., i wsp.
Wydane: (2022)

Smart City Management System Based on Multi-purpose Deep Neural Network
od: Nikolaev, E. I., i wsp.
Wydane: (2022)

Neural Network System for Recognizing Images Affected by Random-Valued Impulse Noise
od: Orazaev, A. R., i wsp.
Wydane: (2023)

A method for planning the trajectory of a mobile robot in an unknown environment with obstacles
od: Tebueva, F. B., i wsp.
Wydane: (2021)

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

System for neural network determination of atrial fibrillation on ecg signals with wavelet-based preprocessing
od: Lyakhov, P. A., i wsp.
Wydane: (2021)

Application of the residue number system to reduce hardware costs of the convolutional neural network implementation
od: Valueva, M. V., i wsp.
Wydane: (2020)

Hardware implementation of a convolutional neural network using calculations in the residue number system
od: Chervyakov, N. I., i wsp.
Wydane: (2019)

Neural network system for analyzing statistical factors of patients for predicting the survival of dental implants
od: Lyakhov, P. A., i wsp.
Wydane: (2023)

Neural network classification system for pigmented skin neoplasms with preliminary hair removal in photographs
od: Lyakhov, P. A., i wsp.
Wydane: (2021)

System for neural network recognition of malignant pigmented skin neoplasms with image pre-processing
od: Lyakhova, U. A., i wsp.
Wydane: (2021)

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

Survey of Optimization Algorithms in Modern Neural Networks
od: Abdulkadirov, R. I., i wsp.
Wydane: (2023)

Discrete Neural Network of Bidirectional Associative Memory
od: Shaposhnikov, A. V., i wsp.
Wydane: (2023)

Analysis of attacks on robotic operation system
od: Azarov, I. V., i wsp.
Wydane: (2022)

Security methods for a group of mobile robots according to the requirements of Russian and foreign legislation
od: Lapina, M. A., i wsp.
Wydane: (2020)