The Significance Of It All: Corporate Disclosure Obligations In Matrixx Initiatives, Inc. v. Siracusano

A Wiener model consists of a linear dynamic system followed by a static nonlinearity. The input and output are measured, but not the intermediate signal. We discuss the Maximum Likelihood estimate for Gaussian measurement and process noise, and the special cases when one of the noise sources is zero

Nordic Crop Wild Relative conservation : A report from two cooperation projects 2015-2019

The report summarizes results from a cooperation among all the Nordic countries during the period 2015 – 2019 (two projects). The work has focused on the conservation of Crop Wild Relatives (CWR), i.e. wild plant species closely related to crops. They are of special importance to humanity since traits of potential value for food security and climate change adaptation can be transferred from CWR into crops. The projects represent the first joint action on the Nordic level regarding in situ conservation of CWR. Substantial progress has been made regarding CWR conservation planning, including development of a Nordic CWR checklist and identification of suitable sites for CWR conservation. A set of recommended future actions was developed, with the most important one being initiation of active in situ conservation of CWR in all Nordic countries

Identification of Wiener Models

The identification task consists of making a model of a system from measured input and output signals. Wiener models consist of a linear dynamic system, followed by a static nonlinearity. We derive an algorithm to calculate the maximum likelihood estimate of the model for this class of systems. We describe an implementation in some detail and show simulation results where a test system is successfully identified from data

Maximum Likelihood Identification of Wiener Models with a Linear Regression Initialization

Many parametric identification routines suffer from the problem with local minima. This is true also for the prediction-error approach to identifying Wiener models, i.e. linear models with a static non-linearity at the output. We here suggest a linear regression initialization, that secures a consistent and efficient estimate, when used in conjunction with a Gauss-Newton minimization scheme. 1 The Prediction Error Identification Estimate A Wiener model consists of a linear dynamic system x(t) = G(q; `)u(t) and a static nonlinearity y(t) = f j (x(t)). Several approaches to the identification of such models have been suggested in the literature. See, e.g., [5, 3, 7, 1]. We shall here look into the prediction error/maximum likelihood method, with the criterion numerically minimized by a Gauss-Newton scheme. The difficulty lies in finding an initialization that avoids the problem with local minima. We shall use an idea from [3] to devise such a consistent initial estimate. We suppose th..

Initialization and model reduction for Wiener model identification

The identification of nonlinear systems by the minimization of a predictionerror criterion suffers from the problem of local minima. To get a reliableestimate we need good initial values for the parameters. In this paper wediscuss the class of nonlinear Wiener models, consisting of a linear dynamicsystem followed by a static nonlinearity. By selecting a parameterizationwhere the parameters enter linearly in the error, we can obtain an initialestimate of the model via linear regression. An example shows that thisapproach may be preferential to trying to estimate the linear system directlyform input-output data, if the input is not Gaussian. We discuss some of theusers choices and how the linear regression initial estimate can be convertedto a desired model structure to use in the prediction error criterionminimization. The method is also applied to experimental data

PBL - något för alla?!

Keywords: teaching, education, problem based learning, PB

PBL - något för alla?!

Keywords: teaching, education, problem based learning, PB

Inconsistency of an Approximate Prediction Error Method for Wiener Model Identification

A Wiener model consists of a linear dynamic block followed by with a nonlinear static block. When identifying the parameters of such a system, the Prediction Error Method (PEM) can be used. Depending on how noise enters the system, the predictor can be difficult to express, and an approximate predictor may be interesting. The estimate obtained from using this approximate predictor is however not always consistent. In this report we investigate this inconsistency