Zobrazeno 1 - 10
of 1 131
pro vyhledávání: '"radial basis function networks"'
Publikováno v:
IET Systems Biology, Vol 18, Iss 4, Pp 119-128 (2024)
Abstract Cancer treatment often involves heat therapy, commonly administered alongside chemotherapy and radiation therapy. The authors address the challenges posed by heat treatment methods and introduce effective control techniques. These approaches
Externí odkaz:
https://doaj.org/article/8f586552b6404e06a4ba54202d938102
Publikováno v:
IET Renewable Power Generation, Vol 18, Iss 8, Pp 1407-1427 (2024)
Abstract The integration of wave energy converters (WECs) into floating offshore wind turbine (FOWT) can effectively reduce costs and increase power generation. When WECs are integrated into FOWT, the hydrodynamic interference, motion coupling, and o
Externí odkaz:
https://doaj.org/article/abeb9d45a24d497b8ebfc20c78637048
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 4, Pp 5545-5568 (2024)
Abstract Deploying static wireless sensor nodes is prone to network coverage gaps, resulting in poor network coverage. In this paper, an attempt is made to improve the network coverage by moving the locations of the nodes. A surrogate-assisted sine P
Externí odkaz:
https://doaj.org/article/ca52079fd47c4bdebf6d00498a9a0ab0
Autor:
Deepak Tiwari, Mehdi Jabbari Zideh, Veeru Talreja, Vishal Verma, Sarika Khushalani Solanki, Jignesh Solanki
Publikováno v:
IEEE Access, Vol 12, Pp 29959-29970 (2024)
Most power systems’ approaches are currently tending towards stochastic and probabilistic methods due to the high variability of renewable sources and the stochastic nature of loads. Conventional power flow (PF) approaches such as forward-backward
Externí odkaz:
https://doaj.org/article/3d36e2636e4a437a8fdc00f9b9221bee
Publikováno v:
IET Control Theory & Applications, Vol 17, Iss 17, Pp 2369-2377 (2023)
Abstract This paper investigates the adaptive robust control problem based on reinforcement learning for an affine nonlinear system with unknown time‐varying uncertainty. Inspired by the ability to estimate uncertainty of neural network, a novel po
Externí odkaz:
https://doaj.org/article/015b3541d25e4843ae7fc10201ca64f5
Autor:
Fabian Wurzberger, Friedhelm Schwenker
Publikováno v:
Entropy, Vol 26, Iss 5, p 368 (2024)
Learning in neural networks with locally-tuned neuron models such as radial Basis Function (RBF) networks is often seen as instable, in particular when multi-layered architectures are used. Furthermore, universal approximation theorems for single-lay
Externí odkaz:
https://doaj.org/article/d1669886f48b422f8c51b80bedaf4eaa
Akademický článek
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Akademický článek
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Publikováno v:
علوم و مهندسی آبیاری, Vol 45, Iss 2, Pp 109-124 (2022)
In this study, Site Groundwater Rating (SGR) in the Amirkabir tunnel has been estimated using Radial Basis Function Networks (RBFNs). SGR is the first rating method that by considering the parameters like joint frequency, joint aperture, schistosity,
Externí odkaz:
https://doaj.org/article/f0323f1d915e4ec78603d8afe73d06d4
Autor:
Dmitry Stenkin, Vladimir Gorbachenko
Publikováno v:
Mathematics, Vol 12, Iss 2, p 241 (2024)
The article is devoted to approximate methods for solving differential equations. An approach based on neural networks with radial basis functions is presented. Neural network training algorithms adapted to radial basis function networks are proposed
Externí odkaz:
https://doaj.org/article/aabcd1d014c84ee8a617e588c9bcf97b