Results 211 to 220 of about 30,328 (257)

Function Emulation Using Radial Basis Function Networks

Neural Networks, 1997
Abstract While learning an unknown input-output task, humans first strive to understand the qualitative structure of the function. Accuracy of performance is then improved with practice. In contrast, existing neural network function approximators do not have an explicit means for abstracting the qualitative structure of a target function.
V. Srinivasa Chakravarthy, Joydeep Ghosh
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Radial-Basis Function Networks

2000
This chapter deals with a special class of artificial neural networks (ANNs) called radial-basis function (RBF) networks. These networks derive their structure and interpretation from the theory of interpolation in multidimensional spaces, and have a mathematical foundation imbedded in regularization theory for solving ill-conditioned problems.
Rao S. Govindaraju, Bin Zhang
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On the Kernel Widths in Radial-Basis Function Networks

Neural Processing Letters, 2003
RBFN (Radial-Basis Function Networks) represent an attractive alternative to other neural network models. Their learning is usually split into an unsupervised part, where center and widths of the basis functions are set, and a linear supervised part for weight computation.
Nabil Benoudjit, Michel Verleysen
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On Monotonic Radial Basis Function Networks

IEEE Transactions on Cybernetics
This article deals with monotonicity conditions for radial basis function (RBF) networks. Two architectures of RBF networks are considered-1) unnormalized network with a local character of the basis function and 2) a normalized network where the value of RBF is taken relatively with respect to the others.
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Learning methods for radial basis function networks

Future Generation Computer Systems, 2005
RBF networks represent a vital alternative to the widely used multilayer perceptron neural networks. In this paper we present and examine several learning methods for RBF networks and their combinations. A gradient-based learning, the three-step algorithm with unsupervised part, and an evolutionary algorithms are introduced, and their performance ...
Roman Neruda, Petra Kudová
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Online Learning in Radial Basis Function Networks

Neural Computation, 1997
An analytic investigation of the average case learning and generalization properties of radial basis function (RBFs) networks is presented, utilizing online gradient descent as the learning rule. The analytic method employed allows both the calculation of generalization error and the examination of the internal dynamics of the network.
Jason A. S. Freeman, David Saad
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The normalized radial basis function neural network

SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218), 2002
Presents a neural network called the normalized radial basis function (NRBF) neural network. The NRBF integrates techniques from two similar neural networks: the general regression neural network (GRNN) and the radial basis function (RBF) neural network.
Felix Heimes, Bram van Heuveln
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Cosine radial basis function neural networks

Proceedings of the International Joint Conference on Neural Networks, 2003., 2004
This paper introduces a new family of reformulated radial basis function (RBF) neural networks, which are referred to as cosine RBFs. These RBF models are developed by relaying upon an axiomatic approach proposed for constructing reformulated RBF neural networks suitable for gradient descent learning.
Mary M. Randolph-Gips, Nicolaos B. Karayiannis   +1 more
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Generalized multiscale radial basis function networks

Neural Networks, 2007
A novel modelling framework is proposed for constructing parsimonious and flexible multiscale radial basis function networks (RBF). Unlike a conventional standard single scale RBF network, where all the basis functions have a common kernel width, the new network structure adopts multiscale Gaussian functions as the bases, where each selected centre has
Stephen A. Billings, Hua-Liang Wei, Michael A. Balikhin   +2 more
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Radial basis function networks in A+

ACM SIGAPL APL Quote Quad, 2002
This paper discusses an implementation and application of Radial Basis Function (RBF) Networks. This type of neural networks performs a universal approach to function approximation. The same algorithm and program may be successfully applied to regression modeling or pattern classification.
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