Transfer function

About: Transfer function is a research topic. Over the lifetime, 14362 publications have been published within this topic receiving 214983 citations. The topic is also known as: system function & network function.

On first-order equations for multidimensional filters

Abstract: A construction is given to obtain first-order equation representations of a multidimensional filter, whose dimension is of the order of the degree of the transfer function.

Identifying linear reduced-order models for systems with arbitrary initial conditions using Prony signal analysis

Abstract: A method of identifying reduced-order linear models for systems operating in the neighborhood of an equilibrium point is presented. The method is based on Prony signal analysis, which has recently received considerable attention in the study of power system electromechanical oscillations. Prior to the application of the input test signal, the system can be in a transient state. The system input test signal is piecewise continuous and allows several Prony analyses to be performed during a transient, with each analysis conducted between input discontinuities. Results of these Prony analyses can be combined in various ways to obtain system eigenvalues, transfer-function residues, and initial condition residues. Two examples are given to illustrate the use of the method. >

Computing Large-Scale System Eigenvalues Most Sensitive to Parameter Changes, With Applications to Power System Small-Signal Stability

Abstract: This paper describes a new algorithm, named the sensitive pole algorithm, for the automatic computation of the eigenvalues (poles) most sensitive to parameter changes in large-scale system matrices. The effectiveness and robustness of the algorithm in tracing root-locus plots is illustrated by numerical results from the small-signal stability analysis of realistic power system models. The algorithm can be used in many other fields of engineering that also study the impact of parametric changes to linear system models.

Black-box identification of mimo transfer functions: Asymptotic properties of prediction error models

Abstract: Identification of multi-input/multi-output (MIMO) transfer functions is considered. The transfer function matrix is parametrized as black-box models which have certain shift properties; no structure or order is chosen a priori. In order to obtain a good transfer function estimate, we allow the order of the model to increase to infinity as the number of data tends to infinity. The expression of asymptotic covariance of the transfer function estimates is derived, which is asymptotic both in the number of data and in the model order. The result indicates that the joint covariance matrix of the transfer function estimates of the process and of the noise filter is proportional to the (generalized) ratio of output noise to imput signal; the factor of proportionality is the ratio of model order to number of data. The result is independent of the particular model structure used. This result is the MIMO extension of the theory of Ljung. The application of this theory for defining the upper bounds of identification errors is highlighted.