Results 21 to 30 of about 59,820 (274)
Energies, 2020 Effectively avoiding methane accidents is vital to the security of manufacturing minerals. Coal mine methane accidents are often caused by a methane concentration overrun, and accurately predicting methane emission quantity in a coal mine is key to ...
Yongkang Yang +3 more
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Risk Prediction Algorithm of Social Security Fund Operation Based on RBF Neural Network
International Journal of Antennas and Propagation, 2021 In order to ensure the benign operation of the social security fund system, it is necessary to understand the social security fund facing all aspects of the risk, more importantly to know the relationship between different risks.
Linxuan Yang
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Parallel implementation of RBF neural networks [PDF]
, 1996 This report presents several parallel implementations, on a MIMD machine, of a learning algorithm called OLS (Orthogonal Least Squares) for RBF (Radial Basis Function) neural networks. The sequential version is first described, and a straightforward parallel version is proposed.
V. Demian +3 more
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An adiabatic neural network for RBF approximation
Neural Computing & Applications, 1994 Numerous studies have addressed nonlinear functional approximation by multilayer perceptrons (MLPs) and RBF networks as a special case of the more general mapping problem. The performance of both these supervised network models intimately depends on the efficiency of their learning process.
Bart Truyen +2 more
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Nanotechnology Reviews, 2022 In this study, we investigated the mechanical properties and chloride ion permeation resistance of geopolymer mortars based on fly ash modified with nano-SiO2 (NS) and polyvinyl alcohol (PVA) fiber and metakaolin (MK) at dose levels of 0–1.2% for PVA ...
Zhang Xuemei +5 more
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Configuring RBF neural networks
Electronics Letters, 1998 A novel method (based on the characteristics of scatter matrices and frequency-sensitive competitive learning) for training the hidden layer of a radial basis function neural network is proposed. The method is demonstrated to be robust and to outperform the state-of-the-art algorithm.
I. Sohn, N. Ansari
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Upset Prediction in Friction Welding Using Radial Basis Function Neural Network
Advances in Materials Science and Engineering, 2013 This paper addresses the upset prediction problem of friction welded joints. Based on finite element simulations of inertia friction welding (IFW), a radial basis function (RBF) neural network was developed initially to predict the final upset for a ...
Wei Liu +3 more
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Financial time series modelling with hybrid model based on customized RBF neural network combined with genetic algorithm [PDF]
, 2014 In this paper, authors apply feed-forward artificial neural network (ANN) of RBF type into the process of modelling and forecasting the future value of USD/CAD time series.
Falát, Lukáš, Marček, Dušan
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Modeling and neural sliding mode control of mems triaxial gyroscope
Advances in Mechanical Engineering, 2022 In this paper, a neural sliding mode control approach is developed to adjust the sliding gain using a radial basis function (RBF) neural network (NN) for the tracking control of Microelectromechanical Systems (MEMS) triaxial vibratory gyroscope.
Yunmei Fang +3 more
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Frontiers in Energy Research, 2022 In this study, a cladding surface temperature prediction method based on an adaptive RBF neural network was proposed. This method can significantly improve the accuracy and efficiency of the thermal safety evaluation of the lead–bismuth fast reactor ...
Hong Wu +4 more
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