Results 221 to 230 of about 30,328 (257)

Radial Basis Function Networks

2018
Radial basis function (RBF) networks represent a fundamentally different architecture from what we have seen in the previous chapters. All the previous chapters use a feed-forward network in which the inputs are transmitted forward from layer to layer in a similar fashion in order to create the final outputs.
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Radial basis function networks for speaker recognition

[Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing, 1991
A speaker recognition system, using a modified form of feedforward neural network based on radial basis functions (RBFs), is presented. Each person to be recognized has his/her own neural model which is trained to recognise spectral feature vectors representative of his/her speech.
John Oglesby, John S. D. Mason
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Multiplication-free radial basis function network

Proceedings of 1995 American Control Conference - ACC'95, 1996
For the purpose of adaptive function approximation, a new radial basis function network is proposed which is nonlinear in its parameters. The goal is to reduce significantly the computational effort for a serial processor, by avoiding multiplication in both the evaluation of the function model and the computation of the parameter adaptation.
Michael Heiss, Stefan Kampl
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Monotonic Normalized Radial Basis Function Networks

2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019
In the paper we address the problem of deriving monotonicity conditions for normalized radial basis function networks. For general shape of the kernels the necessary conditions are expressed as trivial inequalities imposed on the kernel weights together with set of linear inequalities on elements of matrices describing the kernels.
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Radial-basis-function networks: learning and applications

KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516), 2002
We present different training algorithms for radial basis function (RBF) networks. The behaviour of RBF classifiers in three different pattern recognition applications is presented: the classification of 3-D visual objects, high-resolution electrocardiograms and handwritten digits.
Friedhelm Schwenker, Hans A. Kestler, Günther Palm   +2 more
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Radial Basis Function Networks with optimal kernels

2011 IEEE International Symposium on Information Theory Proceedings, 2011
We consider nonlinear function estimation using Radial Basis Function Networks. We analytically determine the optimal radial kernel minimizing the Mean Integrated Square Error (MISE) and the optimal MISE rate of convergence. The rates of convergence for various classes of nonlinear functions and input densities are also considered.
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A Dynamical Architecture for a Radial Basis Function Network

1995
We present a dynamical architecture for a Radial Basis Function Network. The scheme is based on the Simulated Annealing procedure for learning. Increase of performances with respect to classical methods and opportunity to vary the size of the network are reported.
Bernard Lemarié, Anne-Gaelle Debroise
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Monotonicity conditions for radial basis function networks

2018 IEEE Symposium Series on Computational Intelligence (SSCI), 2018
The paper presents a way how to impose monotonicity requirement on radial basis function networks. Monotonicity conditions are expressed as linear constraints on the network weights that enables efficient solving of the related optimization problems. Two illustrative examples are given to demonstrate advantages of incorporation a prior information in ...
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Normalized Gaussian Radial Basis Function networks

Neurocomputing, 1998
Abstract The performances of normalised RBF (NRBF) nets and standard RBF nets are compared in simple classification and mapping problems. In normalized RBF networks, the traditional roles of weights and activities in the hidden layer are switched. Hidden nodes perform a function similar to a Voronoi tessellation of the input space, and the output ...
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Bayesian Radial Basis Function Neural Network

2005
Bayesian radial basis function neural network is presented to explore the weight structure in radial-basis function neural networks for discriminant analysis. The work is motivated by the empirical experiments where the weights often follow certain probability density functions in protein sequence analysis using the bio-basis function neural network ...
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