Journal Staff

In this contribution we describe some of the basic new features of MathWork's System Identification toolbox, version 4.0, which was released in May 1995. The main addition is a graphical user interface (GUI), which allows the user to perform identification, data and model analysis, as well as model validation by less click and mouseless operations. The ideas behind the GUI are explained and its relative merits compared to command driven operations are discussed

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

Bayesian topology identification of linear dynamic networks

In networks of dynamic systems, one challenge is to identify the interconnection structure on the basis of measured signals. Inspired by a Bayesian approach in [1], in this paper, we explore a Bayesian model selection method for identifying the connectivity of networks of transfer functions, without the need to estimate the dynamics. The algorithm employs a Bayesian measure and a forward-backward search algorithm. To obtain the Bayesian measure, the impulse responses of network modules are modeled as Gaussian processes and the hyperparameters are estimated by marginal likelihood maximization using the expectation-maximization algorithm. Numerical results demonstrate the effectiveness of this method

Congressional Power to Control Cotton and Tobacco Production

Identification of systems operating in closed loop has long been of prime interest in industrial applications. The problem offers many possibilities, and also some fallacies, and a wide variety of approaches have been suggested, many quite recently. The purpose of the current contribution is to place most of these approaches in a coherent framework, thereby showing their connections and display similarities and differences in the asymptotic properties of the resulting estimates. The common framework is created by the basic prediction error method, and it is shown that most of the common methods correspond to different parameterizations of the dynamics and noise models. The so-called indirect methods, e.g., are indeed “direct” methods employing noise models that contain the regulator. The asymptotic properties of the estimates then follow from the general theory and take different forms as they are translated to the particular parameterizations. We also study a new projection approach to closed-loop identification with the advantage of allowing approximation of the open-loop dynamics in a given, and user-chosen frequency domain norm, even in the case of an unknown, nonlinear regulator

Regularized system identification using orthonormal basis functions

Most of existing results on regularized system identification focus on regularized impulse response estimation. Since the impulse response model is a special case of orthonormal basis functions, it is interesting to consider if it is possible to tackle the regularized system identification using more compact orthonormal basis functions. In this paper, we explore two possibilities. First, we construct reproducing kernel Hilbert space of impulse responses by orthonormal basis functions and then use the induced reproducing kernel for the regularized impulse response estimation. Second, we extend the regularization method from impulse response estimation to the more general orthonormal basis functions estimation. For both cases, the poles of the basis functions are treated as hyperparameters and estimated by empirical Bayes method. Then we further show that the former is a special case of the latter, and more specifically, the former is equivalent to ridge regression of the coefficients of the orthonormal basis functions.Comment: 6 pages, final submission of an contribution for European Control Conference 2015, uploaded on March 20, 201

Professor Scheppele’s Middle Way: On Minimizing Normativity and Economics in Securities Law

Direct prediction error identification of systems operating in closed loop may lead to biased results due to the correlation between the input and the output noise. The authors study this error, what factors affect it, and how it may be avoided. In particular, the role of the noise model is discussed and the authors show how the noise model should be parameterized to avoid the bias. Apart from giving important insights into the properties of the direct method, this provides a nonstandard motivation for the indirect method

Right to Hearing in License Renewal Proceeding When Allegation is the Subject of Concurrent Rule-Making Proceeding

An overview of central model quality results is given. The focus is on the variance of transfer functions. We look in particular into two questions: (1) Can the variance be smaller than that obtained by direct prediction error/output error? and (2) Can closed loop experiments give estimates with lower variance than open loop ones? The answer to both questions is yes

The Continuity of Statutory and Constitutional Interpretation: An Essay for Phil Frickey

This Essay seeks to honor Phil by exploring the contributions of his Legal Process approach to a problem near and dear to his heart: the uses and legitimacy of canons of statutory construction. I focus, as Phil did in his most recent work, on the canon of constitutional avoidance—that is, the rule that courts should construe statutes to avoid significant ―doubt as to their constitutionality. This Essay largely supports Phil‘s defense of the avoidance canon, but links that defense to another set of canons that Phil has criticized: the various clear statement rules of statutory construction that Phil and Bill Eskridge memorably labeled ―quasi-constitutional law. These rules require that Congress make its intent especially clear when it legislates in areas of particular constitutional sensitivity—for example, by intruding on the prerogatives of the states. This Essay proceeds in three parts. Part I develops two problems in statutory construction—the canon of constitutional avoidance and judge-made clear statement rules—by reference to some major cases decided in the Supreme Court‘s 2008 Term. Part II elaborates the Legal Process School‘s approach to these sorts of problems of canonical construction, with particular emphasis on Professor Frickey‘s work in this vein. Part III then develops the central Legal Process insight that rules of construction are part of constitutional interpretation as a means of interpreting and protecting the broader structural aspects of the Constitution, namely, federalism and separation of powers

Federal Procurement and Equal Employment Opportunity

The paper contains a discussion about what results about the quality of an estimated model can be achieved, if no probabilitic assumptions are introduced. Several technical results that illustrate possibilities and difficulties are also given

Selected Essays on the Conflict of Laws. By Brainerd Currie.

Most subspace identication algorithms are not applicable to closed-loop identication because they require future input to be uncorrelated with pastinnovation. In this paper, we propose a new subspace identication method that remove this requirement by using a parsimonious model formulation with innovation estimation. A simulation example is included to show the effectiveness of the proposed method