Topic
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.
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the authors present a proof of stability of the model reference adaptive control problem for the discrete case, and prove that the stability of this problem is not affected by the model-reference adaptive control model.
Abstract: The paper presents a proof of stability of the model reference adaptive control problem for the discrete case.
242 citations
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TL;DR: In this paper, a predictor can be computed from a frequency-warped autocorrelation function obtained from the power spectrum or by a direct linear transformation of the original acf.
Abstract: Linear prediction is considered with respect to a nonlinear frequency scale obtained by a first‐order all‐pass transformation. The predictor can be computed from a frequency‐warped autocorrelation function obtained from the power spectrum or by a direct linear transformation of the original acf. Three numerical procedures are compared. Alternatively, the predictor can be determined from a covariance matrix or (adaptively) from continuously formed correlations, suitably defined according to the all‐pass transformation. Prediction‐error minimization and spectral flattening are no longer equivalent criteria. In the synthesis part of a vocoder or APC system, no inverse transformation is required, since the direct form of the analysis and synthesis filters can be modified so as to immediately realize the warped transfer function. Single‐word intelligibility is compared for a predictive vocoder on a ’’Bark’’ scale and a linear frequency scale. The Bark scale yields results around 90% even at predictor orders of 5 to 7. More possible applications have been given previously by other authors.
242 citations
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TL;DR: It is shown that the full state feedback and error feedback regulator problems are solvable, under the standard assumptions of stabilizability and detectability, if and only if a pair of regulator equations is solvable.
Abstract: This work extends the geometric theory of output regulation to linear distributed parameter systems with bounded input and output operator, in the case when the reference signal and disturbances are generated by a finite dimensional exogenous system. In particular it is shown that the full state feedback and error feedback regulator problems are solvable, under the standard assumptions of stabilizability and detectability, if and only if a pair of regulator equations is solvable. For linear distributed parameter systems this represents an extension of the geometric theory of output regulation developed in Francis (1997) and Isidori and Byrnes (1990). We also provide simple criteria for solvability of the regulator equations based on the eigenvalues of the exosystem and the system transfer function. Examples are given of periodic tracking, set point control, and disturbance attenuation for parabolic systems and periodic tracking for a damped hyperbolic system.
241 citations
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27 Oct 1996TL;DR: A novel approach to assist the user in exploring appropriate transfer functions for the visualization of volumetric datasets that shields the user from the complex and tedious "trial and error" approach and demonstrates effective and convenient generation of transfer functions.
Abstract: This paper presents a novel approach to assist the user in exploring appropriate transfer functions for the visualization of volumetric datasets. The search for a transfer function is treated as a parameter optimization problem and addressed with stochastic search techniques. Starting from an initial population of (random or pre-defined) transfer functions, the evolution of the stochastic algorithms is controlled by either direct user selection of intermediate images or automatic fitness evaluation using user-specified objective functions. This approach essentially shields the user from the complex and tedious "trial and error" approach, and demonstrates effective and convenient generation of transfer functions.
239 citations
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01 Jan 1987
TL;DR: This book discusses the role of Nonparametric Models in Continuous System Identification, and methods for Obtaining Transfer Functions from nonparametric models using the Frequency-Domain approach.
Abstract: Introduction. Continuous-Time Models of Dynamical Systems. Nonparametric Models. Parametric Models. Stochastic Models of Linear Time-Invariant Systems. Models of Distributed Parameter Systems (DPS). Signals and their Representations. Functions in the Ordinary Sense. Distribution or Generalized Functions. Identification of Linear Time-Invariant (LTIV) Systems via Nonparametric Models. The Role of Nonparametric Models in Continuous System Identification. Test Signals for System Identification. Identification of Linear Time-Invariant Systems - Time-Domain Approach. Frequency-Domain Approach. Methods for Obtaining Transfer Functions from Nonparametric Models. Numerical Transformations between Time- and Frequency-Domains. Parameter Estimation for Continuous-Time Models. The Primary Stage. The Secondary Stage: Parameter Estimation. Identification of Linear Systems Using Adaptive Models. Gradient Methods. Frequency-Domain. Stability Theory. Linear Filters. Identification of Multi-Input Multi-Output (MIMO) Systems, Distributed Parameter Systems (DPS) and Systems with Unknown Delays and Nonlinear Elements. MIMO Systems. Time-Varying Parameter Systems (TVPS). Lumped Systems with Unknown Time-Delays. Identification of Systems with Unknown Nonlinear Elements. Identification of Distributed Parameter Systems. Determination of System Structure. Index.
239 citations