1. Главная
  2. Ресурсы
  3. Библиотечный поиск
Neural network-basedvehicle control insimulatedenvironments usingreal-coded geneticalgorithms Shabalin DenisStanovov Vladimir2025 год

Neural network-based vehicle control in simulated environments using real-coded genetic algorithms : доклад, тезисы доклада
доклад, тезисы доклада, статья из сборника материалов конференций

Дополнительные ссылки: eLibrary · Источник статьи
База данных: Каталог библиотеки СФУ
Библиографическое описание: Neural network-based vehicle control in simulated environments using real-coded genetic algorithms : доклад, тезисы доклада / D. D. Shabalin, V. V. Stanovov. - [S. l. : s. n.], 2025. - Текст : непосредственный // ITM Web of Conferences : III International Workshop / Hybrid Methods of Modeling and Optimization in Complex Systems (HMMOCS-III 2024) (2024 ; 02.12 - 04.12 ; Krasnoyarsk). - Krasnoyarsk, 2025. - P. 5008. - This work was supported by the Ministry of Science and Higher Education of the Russian Federation (grant No. 075-15-2022-1121). - DOI 10.1051/itmconf/20257205008.
Аннотация: This research explores a method of optimizing neural networks for vehicle control in a simulation environment using a real-coded genetic algorithm (RCGA). The study focuses on applying RCGA in conjunction with multiple genetic operators, including simulated binary crossover (SBX), power mutation (PM), and tournament selection, to evolve neural network weights and biases, enhancing control performance for simulated vehicles. By utilizing RCGA to adjust neural network parameters, the approach enables adaptive and efficient vehicle control. The experiments demonstrate that combining sensor data with neuroevolutionary optimization in a simulation leads to a highly reliable control system, achieving performance metrics comparable to human operators. These findings suggest that RCGA-based optimization methods can be effectively applied to complex dynamic systems in various technical fields.
Год издания: 2025
Авторы: Shabalin Denis , Stanovov Vladimir
Издательство: Krasnoyarsk
Источник: ITM Web of Conferences
Номера страниц: 5008
Держатель оригинала документа: Reshetnev Siberian State University of Science and Technology, Siberian Federal University
Мероприятие: Hybrid Methods of Modeling and Optimization in Complex Systems (HMMOCS-III 2024) (Krasnoyarsk, 2024 ; 02.12 - 04.12)
Количество экземпляров:
Твёрдая копия издания отсутствует в фонде библиотеки

Похожие публикации

Evolutionary searchalgorithm forlearning activationfunction of anartificial neuralnetwork Yurshin Viacheslav2025 год

Evolutionary search algorithm for learning activation function of an artificial neural network : доклад, тезисы доклада

  • Yurshin Viacheslav
  • 2025 год
Multi-objectiveparametric synthesisof loss functions inneural networks withevolutionaryalgorithms Morozov EduardStanovov Vladimir2024 год

Multi-objective parametric synthesis of loss functions in neural networks with evolutionary… : доклад, тезисы доклада

  • Morozov Eduard и др.
  • 2024 год
Artificial NeuralNetwork ArchitectureTuning Algorithm Yurshin V. G.Stanovov V. V.2022 год

Artificial Neural Network Architecture Tuning Algorithm : доклад, тезисы доклада

  • Yurshin V. G. и др.
  • 2022 год
The method of optimalresource managementof a distributeddynamic system basedon the algorithm ofzeroing neural… Bryukhanova Evgenia2024 год

The method of optimal resource management of a distributed dynamic system based on the… : доклад, тезисы доклада

  • Bryukhanova Evgenia
  • 2024 год
AUTOMATIC GENERATIONOF NEURAL NETWORKARCHITECTURES BYMULTI-OBJECTIVEGENETIC ALGORITHM Edreev VladimirStanovov Vladimir2025 год

AUTOMATIC GENERATION OF NEURAL NETWORK ARCHITECTURES BY MULTI-OBJECTIVE GENETIC ALGORITHM : научное издание

  • Edreev Vladimir и др.
  • 2025 год
Real-time neuralnetwork system fornon-destructivecontrol of asphaltmixtures compaction Nabizhanov Zh.I.Prokopev A.P.Emelyanov R.T.2021 год

Real-time neural network system for non-destructive control of asphalt mixtures compaction : доклад, тезисы доклада

  • Nabizhanov Zh.I. и др.
  • 2021 год
Multi-CriteriaGenetic Algorithm forOptimizingDistributed ComputingSystems in NeuralNetwork Synthesis Tynchenko Valeriya V.Malashin IvanKurashkin Sergei O.2025 год

Multi-Criteria Genetic Algorithm for Optimizing Distributed Computing Systems in Neural Network… : научное издание

  • Tynchenko Valeriya V. и др.
  • 2025 год
Decoding the NeuralActivity of RecurrentNeural NetworkPlaying a ReflexiveGame Markova G.Bartsev S.2022 год

Decoding the Neural Activity of Recurrent Neural Network Playing a Reflexive Game : доклад, тезисы доклада, статья из…

  • Markova G. и др.
  • 2022 год

Показать ещё

Two-Stage GeneticAlgorithm forOptimizationLogistics Network forGroupage Delivery Malashin Ivan P.Tynchenko Vadim S.Masich Igor S.2024 год

Two-Stage Genetic Algorithm for Optimization Logistics Network for Groupage Delivery : статья из журнала

  • Malashin Ivan P. и др.
  • 2024 год
Soil PropertiesClassification inSustainableAgriculture UsingGeneticAlgorithm-Optimizedand Deep NeuralNetworks Tynchenko YadvigaTynchenko VadimKukartsev Vladislav2024 год

Soil Properties Classification in Sustainable Agriculture Using Genetic Algorithm-Optimized and… : научное издание

  • Tynchenko Yadviga и др.
  • 2024 год
The parallel geneticalgorithm forconstruction oftechnological objectsneural network models Tynchenko V.S.Petrovsky E.A.Tynchenko V.V.2017 год

The parallel genetic algorithm for construction of technological objects neural network models

  • Tynchenko V.S. и др.
  • 2017 год

Hands-on genetic algorithms with Python : applying genetic algorithms to solve real-world deep learning and…

  • Wirsansky Eyal
  • 2020 год
Mobile robotautomatic navigationcontrol algorithmbased on fuzzy neuralnetwork in industrialInternet of things… Pei PingPetrenko Yury N.2020 год

Mobile robot automatic navigation control algorithm based on fuzzy neural network in industrial… : статья из журнала

  • Pei Ping и др.
  • 2020 год
Software forStructure Selectionof an ArtificialNeural Network toControl the InductionSoldering Process Milov A.Tynchenko V.Bukhtoyarov V.2020 год

Software for Structure Selection of an Artificial Neural Network to Control the Induction Soldering Process : доклад,…

  • Milov A. и др.
  • 2020 год
Digital processing ofsatellite imagesusing neural networkalgorithms Kochanov AndreyZolotukhin VyacheslavMironenko Vladislav2022 год

Digital processing of satellite images using neural network algorithms : доклад, тезисы доклада

  • Kochanov Andrey и др.
  • 2022 год
Neural Network-BasedDecoding InputStimulus Data Basedon Recurrent NeuralNetwork NeuralActivity Pattern Bartsev S.I.Baturina P.M.Markova G.M.2022 год

Neural Network-Based Decoding Input Stimulus Data Based on Recurrent Neural Network Neural… : статья из журнала

  • Bartsev S.I. и др.
  • 2022 год