Results 11 to 20 of about 59,339 (255)
Convergent Decomposition Techniques for Training RBF Neural Networks [PDF]
Neural Computation, 2001 In this article we define globally convergent decomposition algorithms for supervised training of generalized radial basis function neural networks. First, we consider training algorithms based on the two-block decomposition of the network parameters into the vector of weights and the vector of centers.
C. BUZZI, L. GRIPPO, SCIANDRONE, MARCO
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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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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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Neural networks based recognition of 3D freeform surface from 2D sketch [PDF]
, 2005 In this paper, the Back Propagation (BP) network and Radial Basis Function (RBF) neural network are employed to recognize and reconstruct 3D freeform surface from 2D freehand sketch.
Qin, SF, Sun, G, Wright, DK
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Gong-kuang zidonghua, 2014 The paper proposed a fault line selection method of small current grounding system based on wavelet de-noising and improved RBF neural network. Fault information matrix is obtained after normalization processing for maximum of absolute value of de-noised
WANG Xiaowei +3 more
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IEEE Access, 2020 With the rapid economic development, urban areas are seeing more and more vehicles, leading to frequent urban traffic congestion. To solve this problem, the forecasting of traffic parameters is essential, in which, road operating speed (hereinafter ...
Chun Ai, Lijun Jia, Mei Hong, Chao Zhang
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Surface profile prediction and analysis applied to turning process [PDF]
, 2008 An approach for the prediction of surface profile in turning process using Radial Basis Function (RBF) neural networks is presented. The input parameters of the RBF networks are cutting speed, depth of cut and feed rate.
COSTES, Jean-Philippe, LU, Chen
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Phase transmittance RBF neural networks
Electronics Letters, 2007 Presented is a new complex valued radial basis function (RBF) neural network with phase transmittance between the input nodes and output, which makes it suitable for channel equalisation on quadrature digital modulation systems.
D.V. Loss +3 more
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Application of improved PSO-RBF neural network in the synthetic ammonia decarbonization
Journal of Hebei University of Science and Technology, 2017 The synthetic ammonia decarbonization is a typical complex industrial process, which has the characteristics of time variation, nonlinearity and uncertainty, and the on-line control model is difficult to be established. An improved PSO-RBF neural network
Yongwei LI +3 more
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