Recursive exact H-infinity identification from impulse-response measurements

We study the H∞-partial realization problem from a behavioral point of view; we give necessary and sufficient conditions for solvability, and a characterization of all solutions. Instrumental in such analysis is the notion of time- and space-symmetrization of the data, which allows to transform the realization problem with metric- and stability constraints into an unconstrained behavioral modeling one

Bayesian kernel-based system identification with quantized output data

In this paper we introduce a novel method for linear system identification with quantized output data. We model the impulse response as a zero-mean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information on regularity and exponential stability. This serves as a starting point to cast our system identification problem into a Bayesian framework. We employ Markov Chain Monte Carlo (MCMC) methods to provide an estimate of the system. In particular, we show how to design a Gibbs sampler which quickly converges to the target distribution. Numerical simulations show a substantial improvement in the accuracy of the estimates over state-of-the-art kernel-based methods when employed in identification of systems with quantized data.Comment: Submitted to IFAC SysId 201

Sparse Volterra and Polynomial Regression Models: Recoverability and Estimation

Volterra and polynomial regression models play a major role in nonlinear system identification and inference tasks. Exciting applications ranging from neuroscience to genome-wide association analysis build on these models with the additional requirement of parsimony. This requirement has high interpretative value, but unfortunately cannot be met by least-squares based or kernel regression methods. To this end, compressed sampling (CS) approaches, already successful in linear regression settings, can offer a viable alternative. The viability of CS for sparse Volterra and polynomial models is the core theme of this work. A common sparse regression task is initially posed for the two models. Building on (weighted) Lasso-based schemes, an adaptive RLS-type algorithm is developed for sparse polynomial regressions. The identifiability of polynomial models is critically challenged by dimensionality. However, following the CS principle, when these models are sparse, they could be recovered by far fewer measurements. To quantify the sufficient number of measurements for a given level of sparsity, restricted isometry properties (RIP) are investigated in commonly met polynomial regression settings, generalizing known results for their linear counterparts. The merits of the novel (weighted) adaptive CS algorithms to sparse polynomial modeling are verified through synthetic as well as real data tests for genotype-phenotype analysis.Comment: 20 pages, to appear in IEEE Trans. on Signal Processin

The Rocketdyne Multifunction Tester. Part 1: Test Method

The Rocketdyne Multifunction Tester is a general purpose test apparatus which utilizes axial and radial magnetic bearings as shaft excitation devices. The tester is modular in design so that different seal and bearing packages can be tested on the same test stand. The tester will be used for rotordynamic coefficient extraction, as well as life and fluid/material compatibility evaluations. Use of a magnetic bearing as a shaft excitation device opens up many possibilities for shaft excitation and rotordynamic coefficient extraction. In addition to describing the basic apparatus, some of the excitation and extraction methods are described. Some of the excitation methods to be discussed include random, aperiodic, harmonic, impulse and chirp

Underdetermined-order recursive least-squares adaptive filtering: The concept and algorithms

Published versio

Absolute calibration and beam reconstruction of MITO (a ground-based instrument in the millimetric region)

An efficient sky data reconstruction derives from a precise characterization of the observing instrument. Here we describe the reconstruction of performances of a single-pixel 4-band photometer installed at MITO (Millimeter and Infrared Testagrigia Observatory) focal plane. The strategy of differential sky observations at millimeter wavelengths, by scanning the field of view at constant elevation wobbling the subreflector, induces a good knowledge of beam profile and beam-throw amplitude, allowing efficient data recovery. The problems that arise estimating the detectors throughput by drift scanning on planets are shown. Atmospheric transmission, monitored by skydip technique, is considered for deriving final responsivities for the 4 channels using planets as primary calibrators.Comment: 14 pages, 6 fiugres, accepted for pubblication by New Astronomy (25 March