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Approximation by nonlinear wavelet networks

[Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing, 1991
By combining the class of feedforward neural networks and results from the wavelet theory, a class of networks call wavelet networks that can be used to approximate any nonlinear function is proposed. A stochastic gradient procedure for black-box identification of nonlinear static systems based on this class of networks is developed.
Qinghua Zhang, Albert Benveniste
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Nonlinear approximation

Acta Numerica, 1998
This is a survey of nonlinear approximation, especially that part of the subject which is important in numerical computation. Nonlinear approximation means that the approximants do not come from linear spaces but rather from nonlinear manifolds. The central question to be studied is what, if any, are the advantages of nonlinear approximation over the ...
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Approximations for Nonlinear Filters based on Quantisation

Monte Carlo Methods and Applications, 2001
The author studies the use of observation quantisation with discrete-time nonlinear filters in which the signal is a finite-state Markov chain, and the observation is the signal plus white noise. It is shown that the reduction of the filter accuracy is small for filters with slow signal dynamics and substantially noisy observations.
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Stochastic Approximation and Nonlinear Regression