Search results for "System identification"

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

Simulation of parallel mechanisms for motion cueing generation in vehicle simulators using AM-FM bi-modulated signals

2018

Abstract The use of robotic motion platforms in vehicle simulators is relatively common. However, the process of testing and tuning the so-called washout algorithms, used for motion cueing generation in motion-based vehicle simulators, is complex. This process can be reduced in cost, simplified, improved, shortened and performed safer if virtual motion platforms are used instead of real devices. This paper deals with identifying a method to perform a fast but reliable simulation of parallel mechanisms to be used for motion cueing generation. The method relies on the use of Laplacian polynomial transfer function models by means of using AM-FM bi-modulated signals as reference inputs to achie…

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…

Dynamical identification of building structures: new strategies

2010

In this paper the evolution of a time domain dynamic identification technique based on a statistical moment approach is presented. This technique can be used in the case of structures under base random excitations in the linear state and in the non linear one. By applying 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 of obtaining the identification of the dissipation characteristics independen…

Soft Sensor Design, Transferability and Causality through Machine Learning Techniques

2023

Support Vector Machines Framework for Linear Signal Processing

2005

This paper presents a support vector machines (SVM) framework to deal with linear signal processing (LSP) problems. The approach relies on three basic steps for model building: (1) identifying the suitable base of the Hilbert signal space in the model, (2) using a robust cost function, and (3) minimizing a constrained, regularized functional by means of the method of Lagrange multipliers. Recently, autoregressive moving average (ARMA) system identification and non-parametric spectral analysis have been formulated under this framework. The generalized, yet simple, formulation of SVM LSP problems is particularized here for three different issues: parametric spectral estimation, stability of I…

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

2018

This is the second part of a paper, 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 off-line identification of its parameters. The first part has treated the theoretical framework of the model. This second part is devoted to the description of an identification technique which has been suitably developed for the estimation of the parameters of the LIM dynamic model accounting for both the dynamic end-effects and iron losses, described in the first part of the paper. Such an identification technique is strictly related to the state …

An Algorithm for Parameter Identification of UAS from Flight Data

2014

The aim of the present work is to realize an identification algorithm especially devoted to UAS (unmanned aerial systems). Because UAS employ low cost sensor, very high measurement noise has to be taken into account. Therefore, due to both modelling errors and atmospheric turbulence, noticeable system noise has also to be considered. To cope with both the measurement and system noise, the identification problem addressed in this work is solved by using the FEM (filter error method) approach. A nonlinear mathematical model of the subject aircraft longitudinal dynamics has been tuned up through semi-empirical methods, numerical simulations and ground tests. To take into account model nonlinea…

Identification of multi degree of freedom civil systems under base lateral random forces by using potential models

2005

An identifiable model to assess frequency-domain Granger causality in the presence of significant instantaneous interactions

2010

We present a new approach for the investigation of Granger causality in the frequency domain by means of the partial directed coherence (PDC). The approach is based on the utilization of an extended multivariate autoregressive (MVAR) model, including instantaneous effects in addition to the lagged effects traditionally studied, to fit the observed multiple time series prior to PDC computation. Model identification is performed combining standard MVAR coefficient estimation with a recent technique for instantaneous causal modeling based on independent component analysis. The approach is first validated on simulated MVAR processes showing that, in the presence of instantaneous effects, only t…