Neural Computation
Regularization theory and neural networks architectures
Neural Computation
Nonparametric input estimation in physiological systems: problems, methods, and case studies
Automatica (Journal of IFAC)
Self-whitening algorithms for adaptive equalization anddeconvolution
IEEE Transactions on Signal Processing
Multichannel system identification and deconvolution: performancebounds
IEEE Transactions on Signal Processing
Regularization networks: fast weight calculation via Kalman filtering
IEEE Transactions on Neural Networks
Bayes and empirical Bayes semi-blind deconvolution using eigenfunctions of a prior covariance
Automatica (Journal of IFAC)
Wavelet estimation by Bayesian thresholding and model selection
Automatica (Journal of IFAC)
An inequality constrained nonlinear Kalman-Bucy smoother by interior point likelihood maximization
Automatica (Journal of IFAC)
Brief paper: Fast computation of smoothing splines subject to equality constraints
Automatica (Journal of IFAC)
Input estimation in nonlinear dynamical systems using differential algebra techniques
Automatica (Journal of IFAC)
Distributed Kalman smoothing in static Bayesian networks
Automatica (Journal of IFAC)
Hi-index | 22.16 |
Linear inverse problems with discrete data are equivalent to the estimation of the continuous-time input of a linear dynamical system from samples of its output. The solution obtained by means of regularization theory has the structure of a neural network similar to classical RBF networks. However, the basis functions depend in a nontrivial way on the specific linear operator to be inverted and the adopted regularization strategy. By resorting to the Bayesian interpretation of regularization, we show that such networks can be implemented rigorously and efficiently whenever the linear operator admits a state-space representation. An analytic expression is provided for the basis functions as well as for the entries of the matrix of the linear system used to compute the weights. The results are illustrated through a deconvolution problem where the spontaneous secretory rate of luteinizing hormone (LH) of the hypophisis is reconstructed from measurements of plasma LH concentrations.