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Neural network approach for the calculation of potential coefficients in quantum mechanics
(Elsevier, 2017)
A numerical method based on artificial neural networks is used to solve the inverse Schrödinger equation for a multi-parameter class of potentials. First, the finite element method was used to solve repeatedly the direct ...
Generating Random Variates via Kernel Density Estimation and Radial Basis Function Based Neural Networks
(Lecture Notes in Computer Science, 2019)
A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function
(Springer-verlag Berlin, 2008-01-01)
Spiking neural networks - networks that encode information in the timing of spikes - are arising as a new approach in the artificial neural networks paradigm, emergent from cognitive science. One of these new models is the ...
A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function
(Springer-verlag Berlin, 2008-01-01)
Spiking neural networks - networks that encode information in the timing of spikes - are arising as a new approach in the artificial neural networks paradigm, emergent from cognitive science. One of these new models is the ...
Application of artificial neural networks to the classification of soils from Sao Paulo state using near-infrared spectroscopy
(Royal Soc ChemistryCambridgeInglaterra, 2001)
Traffic flow breakdown prediction using feature reduction through Rough-Neuro fuzzy Networks
(2011-10-24)
The prediction of the traffic behavior could help to make decision about the routing process, as well as enables gains on effectiveness and productivity on the physical distribution. This need motivated the search for ...
Traffic flow breakdown prediction using feature reduction through Rough-Neuro fuzzy Networks
(2011-10-24)
The prediction of the traffic behavior could help to make decision about the routing process, as well as enables gains on effectiveness and productivity on the physical distribution. This need motivated the search for ...
Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi's orthogonal arrays
(Pergamon-Elsevier B.V. Ltd, 2012-07-01)
This work presents a study on the applicability of radial base function (RBF) neural networks for prediction of Roughness Average (R-a) in the turning process of SAE 52100 hardened steel, with the use of Taguchi's orthogonal ...
Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi's orthogonal arrays
(Pergamon-Elsevier B.V. Ltd, 2012-07-01)
This work presents a study on the applicability of radial base function (RBF) neural networks for prediction of Roughness Average (R-a) in the turning process of SAE 52100 hardened steel, with the use of Taguchi's orthogonal ...
Satisfiability Logic Analysis Via Radial Basis Function Neural Network with Artificial Bee Colony Algorithm
Radial Basis Function Neural Network (RBFNN) is a variant of artificial neural network (ANN) paradigm, utilized in a plethora of fields of studies such as engineering, technology and science. 2 Satisfiability (2SAT) ...