Search results for "system identification"

A framework for assessing frequency domain causality in physiological time series with instantaneous effects.

2013

We present an approach for the quantification of directional relations in multiple time series exhibiting significant zero-lag interactions. To overcome the limitations of the traditional multivariate autoregressive (MVAR) modelling of multiple series, we introduce an extended MVAR (eMVAR) framework allowing either exclusive consideration of time-lagged effects according to the classic notion of Granger causality, or consideration of combined instantaneous and lagged effects according to an extended causality definition. The spectral representation of the eMVAR model is exploited to derive novel frequency domain causality measures that generalize to the case of instantaneous effects the kno…

A strategy for the identification of building structures under base excitations

2008

In this paper the evolution of a time domain dynamic identification technique based on a statistical moment approach is presented. This technique is usable in the case of structures under base random excitations in the linear state and in the non linear one. By applying Itoˆ stochastic calculus special algebraic equations can be obtained depending on the statistical moments of the response of the system to be identified. Such equations can be used for the dynamic identification of the mechanical parameters and of the input. The above equations, differently from many techniques in the literature, show the possibility to obtain the identification of the dissipation characteristics independent…

Assessment of Granger causality by nonlinear model identification: application to short-term cardiovascular variability.

2007

A method for assessing Granger causal relationships in bivariate time series, based on nonlinear autoregressive (NAR) and nonlinear autoregressive exogenous (NARX) models is presented. The method evaluates bilateral interactions between two time series by quantifying the predictability improvement (PI) of the output time series when the dynamics associated with the input time series are included, i.e., moving from NAR to NARX prediction. The NARX model identification was performed by the optimal parameter search (OPS) algorithm, and its results were compared to the least-squares method to determine the most appropriate method to be used for experimental data. The statistical significance of…

Non-linear System Identification with Composite Relevance Vector Machines

2007

Nonlinear system identification based on relevance vector machines (RVMs) has been traditionally addressed by stacking the input and/or output regressors and then performing standard RVM regression. This letter introduces a full family of composite kernels in order to integrate the input and output information in the mapping function efficiently and hence generalize the standard approach. An improved trade-off between accuracy and sparsity is obtained in several benchmark problems. Also, the RVM yields confidence intervals for the predictions, and it is less sensitive to free parameter selection. Teoría de la Señal y Comunicaciones

Modeling and field-experiments identification of vertical dynamics of vehicle with active anti-roll bar

2020

This paper deals with modeling and identification of vertical dynamics of the ground vehicle equipped with two active anti-roll torsion bars. A series of field tests of a full-scale drive have been performed, from which multiple displacement and acceleration data of the unsprung and sprung masses have been collected for each vehicle corner. The standard full vertical vehicle model is extended by the developed model of an active anti-roll torsion bar and valve-controlled semi-active shock absorbing damper. Along with the three-dimensional damping map, the nonlinear progressive stiffness of the elastomer-based decoupling unit are identified from the available data. The multi-channel and multi…

Soft Sensor Design, Transferability and Causality through Machine Learning Techniques

2023

The Hu-Washizu variational principle for the identification of imperfections in beams

2008

This paper presents a procedure for the identification of imperfections of structural parameters based on displacement measurements by static tests. The proposed procedure is based on the well-known Hu–Washizu variational principle, suitably modified to account for the response measurements, which is able to provide closed-form solutions to some inverse problems for the identification of structural parameter imperfections in beams. Copyright © 2008 John Wiley & Sons, Ltd.

LPV Model Identification For The Stall And Surge Control of a Jet Engine

2001

Abstract The problem of identifying discrete-time Linear Parameter Varying (LPV) models of non-linear or time-varying systems for gain scheduling control is considered assuming that inputs, outputs and the scheduling parameters are measured, and a form of the functional dependence of the coefficients on the parameters is known. The identification procedure is applied to the controlled model of compressors for jet engines. The model is controlled in order to avoid rotating stall and surge. Aim of the present paper is to identify the LPV model based on the nonlinear model of compressors in order to design a robust gain scheduling predictive controller.

State Space-Vector Model of Linear Induction Motors Including End-effects and Iron Losses - Part II: Model Identification and Results

2020

This is the second part of an article, divided into two parts, dealing with the definition of a space-vector dynamic model of the linear induction motor (LIM) taking into consideration both the dynamic end-effects and the iron losses as well as the offline identification of its parameters. This second part is devoted to the description of an identification technique that has been suitably developed for the estimation of the electrical parameters of the LIM dynamic model accounting for both the dynamic end-effects and iron losses. Such an identification technique is strictly related to the state formulation of the proposed model and exploits genetic algorithms for minimizing a suitable cost …

Learning non-linear time-scales with kernel -filters

2009

A family of kernel methods, based on the @c-filter structure, is presented for non-linear system identification and time series prediction. The kernel trick allows us to develop the natural non-linear extension of the (linear) support vector machine (SVM) @c-filter [G. Camps-Valls, M. Martinez-Ramon, J.L. Rojo-Alvarez, E. Soria-Olivas, Robust @c-filter using support vector machines, Neurocomput. J. 62(12) (2004) 493-499.], but this approach yields a rigid system model without non-linear cross relation between time-scales. Several functional analysis properties allow us to develop a full, principled family of kernel @c-filters. The improved performance in several application examples suggest…