On the Relation between Discrete and Continuous-time Refined Instrumental Variable Methods

The Refined Instrumental Variable method for discrete-time systems (RIV) and its variant for continuous-time systems (RIVC) are popular methods for the identification of linear systems in open-loop. The continuous-time equivalent of the transfer function estimate given by the RIV method is commonly used as an initialization point for the RIVC estimator. In this letter, we prove that these estimators share the same converging points for finite sample size when the continuous-time model has relative degree zero or one. This relation does not hold for higher relative degrees. Then, we propose a modification of the RIV method whose continuous-time equivalent is equal to the RIVC estimator for any non-negative relative degree. The implications of the theoretical results are illustrated via a simulation example.</p

Consistency analysis of refined instrumental variable methods for continuous-time system identification in closed-loop

Refined instrumental variable methods have been broadly used for identification of continuous-time systems in both open and closed-loop settings. However, the theoretical properties of these methods are still yet to be fully understood when operating in closed-loop. In this paper, we address the consistency of the simplified refined instrumental variable method for continuous-time systems (SRIVC) and its closed-loop variant CLSRIVC when they are applied on data that is generated from a feedback loop. In particular, we consider feedback loops consisting of continuous-time controllers, as well as the discrete-time control case. This paper proves that the SRIVC and CLSRIVC estimators are not generically consistent when there is a continuous-time controller in the loop, and that generic consistency can be achieved when the controller is implemented in discrete-time. Numerical simulations are presented to support the theoretical results

Parametric Continuous-Time Blind System Identification

In this paper, the blind system identification problem for continuous-time systems is considered. A direct continuous-time estimator is proposed by utilising a state-variable-filter least squares approach. In the proposed method, coupled terms between the numerator polynomial of the system and input parameters appear in the parameter vector which are subsequently separated using a rank-1 approximation. An algorithm is then provided for the direct identification of a single-input single-output linear time-invariant continuous-time system which is shown to satisfy the property of correctness under some mild conditions. Monte Carlo simulations demonstrate the performance of the algorithm and verify that a model and input signal can be estimated to a proportion of their true values

Statistical Analysis of Block Coordinate Descent Algorithms for Linear Continuous-time System Identification

Block coordinate descent is an optimization technique that is used for estimating multi-input single-output (MISO) continuous-time models, as well as single-input single output (SISO) models in additive form. Despite its widespread use in various optimization contexts, the statistical properties of block coordinate descent in continuous-time system identification have not been covered in the literature. The aim of this paper is to formally analyze the bias properties of the block coordinate descent approach for the identification of MISO and additive SISO systems. We characterize the asymptotic bias at each iteration, and provide sufficient conditions for the consistency of the estimator for each identification setting. The theoretical results are supported by simulation examples

A case–control study to assess the effectiveness of pertussis vaccination during pregnancy on newborns, Valencian community, Spain, 1 March 2015 to 29 February 2016

In the Valencian Community (Spain), the programme of maternal pertussis vaccination during pregnancy started in January 2015. The objective of this study was to estimate in this region the vaccine effectiveness (VE) in protecting newborns against laboratory-confirmed pertussis infection. A matched case–control study was undertaken in the period between 1 March 2015 and 29 February 2016. Twenty-two cases and 66 controls (+/− 15 days of age difference) were included in the study. Cases were non-vaccinated infants < 3 months of age at disease onset testing positive for pertussis by real-time PCR. For every case three unvaccinated controls were selected. Odds ratios (OR) were calculated by multiple conditional logistic regression for association between maternal vaccination and infant pertussis. Other children in the household, as well as mother- and environmental covariates were taken into account. The VE was calculated as 1 − OR. Mothers of five cases (23%) and of 41 controls (62%) were vaccinated during pregnancy. The adjusted VE was 90.9% (95% confidence interval (CI): 56.6 to 98.1). The only covariate in the final model was breastfeeding (protective effect). Our study provides evidence in favour of pertussis vaccination programmes for pregnant women in order to prevent whooping cough in infants aged less than 3 months