Application of Spiking Neural Networks for Modelling the Process of High-Temperature Hydrogen Production in Systems With Gas-Cooled Reactors *

Nuclear Energy and Technology
doi 10.3897/nucet.5.36474
Full Text
Abstract

Available in full text

Date
Authors
Publisher

Pensoft Publishers

Related search

Figure 5 From: Starkov SO, Lavrenkov YN (2019) Application of Spiking Neural Networks for Modelling the Process of High-Temperature Hydrogen Production in Systems With Gas-Cooled Reactors *. Nuclear Energy and Technology 5(2): 39-47.

2019English

Figure 1 From: Starkov SO, Lavrenkov YN (2019) Application of Spiking Neural Networks for Modelling the Process of High-Temperature Hydrogen Production in Systems With Gas-Cooled Reactors *. Nuclear Energy and Technology 5(2): 39-47.

2019English

THATCH: A Computer Code for Modelling Thermal Networks of High- Temperature Gas-Cooled Nuclear Reactors

1991English

Validation of SCALE for High Temperature Gas-Cooled Reactors Analysis

2012English

Nuclear Hydrogen Production Based on High Temperature Gas Cooled Reactor in China

Chinese Journal of Engineering Science
2019English

Power Cycle Assessment of Nuclear High Temperature Gas-Cooled Reactors

Applied Thermal Engineering
Power TechnologyIndustrialManufacturing EngineeringEnergy Engineering
2009English

Analysis of the ReSuMe Learning Process for Spiking Neural Networks

International Journal of Applied Mathematics and Computer Science
EngineeringComputer ScienceApplied Mathematics
2008English

Artificial Neural Networks for Controlling the Temperature of Internally Cooled Turning Tools

Modern Mechanical Engineering
2013English

Assessment of Very High-Temperature Reactors in Process Applications. Appendix III. Engineering Evaluation of Process Heat Applications for Very-High Temperature Reactors

1977English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2024 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy