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Enhanced forecasting of friction and cohesion of augmented unsaturated soil with nanostructured quarry fines (NQF) addition. [PDF]
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An Incremental Design of Radial Basis Function Networks
IEEE Transactions on Neural Networks and Learning Systems, 2014This paper proposes an offline algorithm for incrementally constructing and training radial basis function (RBF) networks. In each iteration of the error correction (ErrCor) algorithm, one RBF unit is added to fit and then eliminate the highest peak (or lowest valley) in the error surface. This process is repeated until a desired error level is reached.
Tiantian Xie +2 more
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Visualization of radial basis function networks
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 2003Presents a method for the 3D visualization of the structure of radial basis function networks. This method allows the visualization of basis function characteristics (centers and widths) along with second level weights. Network properties can be displayed simultaneously with the training data or test data in the same input space.
Adrian K. Agogino +2 more
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Approximation and Radial-Basis-Function Networks
Neural Computation, 1993This paper concerns conditions for the approximation of functions in certain general spaces using radial-basis-function networks. It has been shown in recent papers that certain classes of radial-basis-function networks are broad enough for universal approximation. In this paper these results are considerably extended and sharpened.
Jooyoung Park, Irwin W. Sandberg
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Multi-layer radial basis function networks. An extension to the radial basis function
Proceedings of International Conference on Neural Networks (ICNN'96), 2002This paper presents the initial research carried out into a new neural network called the multilayer radial basis function network (MRBF). The network extends the radial basis function (RBF) in a similar way to that in which the multilayer perceptron extends the perceptron.
R. J. Craddock, K. Warwick
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Generalization Performance of Radial Basis Function Networks
IEEE Transactions on Neural Networks and Learning Systems, 2015This paper studies the generalization performance of radial basis function (RBF) networks using local Rademacher complexities. We propose a general result on controlling local Rademacher complexities with the L1 -metric capacity. We then apply this result to estimate the RBF networks' complexities, based on which a novel estimation error bound is ...
Yunwen Lei, Lixin Ding, Wensheng Zhang
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FRBF: A Fuzzy Radial Basis Function Network
Neural Computing & Applications, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sushmita Mitra, Jayanta Basak
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Radial Basis Function Networks
2013Learning is an approximation problem, which is closely related to the conventional approximation techniques, such as generalized splines and regularization techniques. The RBF network has its origin in performing exact interpolation of a set of data points in a multidimensional space [81].
Ke-Lin Du, M. N. S. Swamy
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Radial Basis Functions Networks
2002The solution of complex mapping problems with artificial neural networks normally demands the use of a multi-layer network structure. This multi-layer topology process data into consecutive steps in each one of the layers. Radial Basis Functions networks are a particular neural network structure that uses radial functions in the intermediate, or hidden,
A. Braga +4 more
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