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In this paper a method is proposed that allows the identification of input-output quasi-linear parameter-varying (LPV) models based on ergodic signals. In this case the use of the instrumental variables (IV) method leads to a consistent estimation of the quasi-LPV model parameters. Moreover, an indirect closed-loop identification technique, which has been proposed for the identification of linear...
Some proofs concerning a subspace identification algorithm are presented. It is proved that the Kalman filter gain and the noise innovations process can be identified directly from known input and output data without explicitly solving the Riccati equation. Furthermore, it is in general and for colored inputs, proved that the subspace identification of the states only is possible if the deterministic...
This paper provides a necessary and sufficient condition for detectability, and an explicit construction of observers when this condition is satisfied, for chemical reaction networks of the Feinberg-Horn-Jackson zero deficiency type.
The Virtual Reference Feedback Tuning (VRFT) is a data based method for the design of feedback controllers; it has been presented by the same authors in previous work. In the original formulation the VRFT method gives a solution to the one degree of freedom model-reference control problem in which the objective is to shape the I/O transfer function of the designed control system. This paper delivers...
In this paper, we present a method for estimating the signal sources steering vector using an arbitrary planar array with omnidirectional elements. The proposed method improves the initial estimation of the signal steering vector in two steps. In the first step of this algorithm we minimize of the distance between the steering vector and the signal subspace. The second step improves the estimation...
Adaptive filters with suitable nonlinear devices are very effective in suppressing the adverse effect due to impulse noise. In a previous work, the authors have proposed a new class of nonlinear adaptive filters using the concept of robust statistics [1,2]. The robust M-estimator is used as the objective function, instead of the mean square errors, to suppress the impulse noise. The optimal coefficient...
A multidimensional filtering technique is proposed using fuzzy logic ideas and based on local statistics. The local multivariate histogram of the mutlichannel image is computed using the Parzen estimation technique. The maximum and minimum of the histogram are used as parameters, to describe the signal shape. The method is organized around a fuzzy control system. Experimental results, in true color...
In this paper, we propose a frequency domain active noise control (ANC) system without a secondary path model. The proposed system is based on the frequency domain simultaneous perturbation (FDSP) method we have proposed. In this system, the coefficients of the adaptive filter are updated only by error signals. The conventional ANC system using the filtered-x algorithm becomes unstable due to the...
We propose a novel method for applying real-valued independent component analysis (ICA) to complex-valued multi-sensor data that comprises instantaneous linear mixtures of co-channel non-Gaussian independent signals. We examine, for non-ideal practical conditions, the optimahty of cumulant-based ICA and blind signal separation approaches in terms of the standard criterion for statistical independence...
This paper provides a way to classify vocal disorders for clinical applications, thanks to the idea of geometric signal separation in a feature space. It is well known that the human voice source generates complex signals including subharmonics and toroidal oscillations. Typical chaotic quantities — like the entropy and the dimension of the attractor — together with autocorrelation function, power...
The problem of finite word length implementation is discussed in this paper. Alternatively to the ρDFIIt recently proposed by G. Li et al., and leaning on the specialized implicit form for a unified analysis, a new effective and sparse structure, named ρ-modal realization, is developed. This realization meets simultaneously accuracy (low sensitivity, round-off noise gain and overflow risk), few and...
The estimation approach discussed in this paper is based on a signal-theoretic and statistical analysis of the notion of orientation. In contrast to other approaches, it does not require the computation of gray value gradients, or the power spectrum of the given signal patch, or quadrature filter outputs, but directly estimates a small central part of the autocovariance function (acf) of the signal...
In this paper, we address the problem of robust moving horizon observer for nonlinear discrete-time systems. The main contribution lies in the use of a robust moving horizon approach in a Luenberger structure observer. Thanks to this new design, a new nonrestrictive synthesis condition, expressed in term of Bilinear Matrix Inequality (BMI), is obtained. Indeed, the obtained BMI contains more degree...
This paper proposes a method for fault diagnosis of dynamic processes using the multiple model approach. The technique presented concerns the identification of a non-linear dynamic system based on Takagi-Sugeno (TS) fuzzy models. It can be shown that any non-linear dynamic process can, in fact, be described as a composition of several TS models selected according to process operating conditions. In...
The uniform DFT filter bank has been used routinely in discrete multitone modulation (DMT) systems because of implementation efficiency. It has recently been shown that principal component filter banks (PCFB) which are known to be optimal for data compression and denoising applications, are also optimal for a number of criteria in DMT communication. In this paper we show that such filter banks are...
A parameter estimation algorithm is developed for the identification of an input output quadratic model. The excitation is a zero mean white Gaussian input and the output is corrupted by additive measurement noise. Input output crosscumulants up to fifth order are employed and the identification problem of the unknown model parameters is reduced to the solution of succesive linear systems of equations...
Reset compensation has been used to overcome limitations of LTI compensation. However, since a reset compensator may destabilize a stable base LTI system, stability needs to be guaranteed for a proper application of reset control. The goal of this work is the study of the stability of a nonlinear/hybrid compensator previously developed by some of the authors, referred to as the PI+CI compensator....
In this paper, we present an exact performances analysis of adaptive filtering without any unrealistic hypothesis. In particular, we demonstarte that convergence rate depends on the high order statistics of the input. Consequently, in some cases, adaptive algorithms with decor-relating properties do not speed up the convergence as expected. In fact, we give example showing that the convergence for...
The paper considers stabilization under communication errors and limited data rate by means of realistic controllers with bounded (as time progresses) computational powers. Discrete-time partially observed noisy linear systems are studied for which the sensor signals are communicated to the controller over a finite capacity stochastic digital link. Addressed is stabilization in probability. It is...
Recursive sparse parameter estimates obtained using the author's recent maximum a posteriori (MAP) approach, where the sparse parameter estimates are determined as the a posteriori mode of a Gaussian sum filter, are compared with a new maximum probability (MP) methodology, where the sparse parameter estimates are determined as the component of a Gaussian sum filter with the highest a posteriori weighting...
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