Results 21 to 30 of about 13,817 (251)
An online self-adaptive RBF network algorithm based on the Levenberg-Marquardt algorithm
Applied Artificial Intelligence, 2022 Aiming at the problem that the Levenberg-Marquardt (LM) algorithm can not train online radial basis function (RBF) neural network and the deficiency in the RBF network structure design methods, this paper proposes an online self-adaptive algorithm for ...
ZhaoZhao Zhang +3 more
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Integrated neural network model with pre-RBF kernels
Science Progress, 2021 To improve the network performance of radial basis function (RBF) and back-propagation (BP) networks on complex nonlinear problems, an integrated neural network model with pre-RBF kernels is proposed.
Hui Wen +3 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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A Novel Adaptive Kernel for the RBF Neural Networks [PDF]
Circuits, Systems, and Signal Processing, 2016 In this paper, we propose a novel adaptive kernel for the radial basis function (RBF) neural networks. The proposed kernel adaptively fuses the Euclidean and cosine distance measures to exploit the reciprocating properties of the two. The proposed framework dynamically adapts the weights of the participating kernels using the gradient descent method ...
Shujaat Khan +3 more
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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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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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Frontiers in Earth Science, 2023 Risk assessment is critical to ensure the safe operation of oil and gas pipeline systems. The core content of such risk assessment is to determine the failure probability of the pipelines quantitatively and accurately.
Lexin Zhao +3 more
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A Novel Kernel for RBF Based Neural Networks [PDF]
Abstract and Applied Analysis, 2014 Radial basis function (RBF) is well known to provide excellent performance in function approximation and pattern classification. The conventional RBF uses basis functions which rely on distance measures such as Gaussian kernel of Euclidean distance (ED) between feature vector and neuron’s center, and so forth.
Wasim Aftab +2 more
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