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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.


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Journal ArticleDOI
TL;DR: This paper presents a fully complex-valued relaxation network (FCRN) with its projection-based learning algorithm and demonstrates the superior classification/approximation performance of the FCRN.
Abstract: This paper presents a fully complex-valued relaxation network (FCRN) with its projection-based learning algorithm. The FCRN is a single hidden layer network with a Gaussian-like sech activation function in the hidden layer and an exponential activation function in the output layer. For a given number of hidden neurons, the input weights are assigned randomly and the output weights are estimated by minimizing a nonlinear logarithmic function (called as an energy function) which explicitly contains both the magnitude and phase errors. A projection-based learning algorithm determines the optimal output weights corresponding to the minima of the energy function by converting the nonlinear programming problem into that of solving a set of simultaneous linear algebraic equations. The resultant FCRN approximates the desired output more accurately with a lower computational effort. The classification ability of FCRN is evaluated using a set of real-valued benchmark classification problems from the University of California, Irvine machine learning repository. Here, a circular transformation is used to transform the real-valued input features to the complex domain. Next, the FCRN is used to solve three practical problems: a quadrature amplitude modulation channel equalization, an adaptive beamforming, and a mammogram classification. Performance results from this paper clearly indicate the superior classification/approximation performance of the FCRN.

56 citations

Journal ArticleDOI
TL;DR: In this paper, a Gaussian frequency-domain maximum likelihood estimator (MLE) was used to estimate the transfer function of linear continuous-time systems with time delay, which is an errors-in-variables model, which means that the input as well as the output of the system is disturbed with noise.
Abstract: A Gaussian frequency-domain maximum likelihood estimator (MLE) to estimate the transfer function of linear continuous-time systems with time delay is presented. The stochastic framework is an errors-in-variables model, which means that the input as well as the output of the system is disturbed with noise. The estimator is applied to a practical measurement problem, namely the estimation of the location of discontinuities, e.g. faults in electrical cables from a reflectogram. Experimental results for coaxial lines show that it is possible to identify simultaneously the location of the discontinuity and a rational approximation of the generator mismatch, the fault impedance, and some of the cable parameters. >

56 citations

Journal ArticleDOI
TL;DR: In this paper, a linear system analysis applied to groundwater flow is presented as an alternative modeling technique to traditional discretized groundwater models (i.e., finite-difference and finite-element), which require elaborate parameters and boundary conditions.

56 citations

Journal ArticleDOI
TL;DR: In this paper, the problem of robust H∞ control for uncertain linear neutral delay systems is considered and a sufficient condition for the solvability of the above problem is proposed.
Abstract: This paper deals with the problem of robust H∞ control for uncertain linear neutral delay systems. The parameter uncertainty under consideration is assumed to be norm-bounded time-invariant and appears in all the matrices of the state-space model. The problem we address is the design of memoryless state feedback controllers such that the closed-loop system is asymptotically stable and the H∞ norm of the closed-loop transfer function from disturbance to the controlled output is strictly less than a prescribed positive scalar for all admissible uncertainties. In terms of a linear matrix inequality (LMI), a sufficient condition for the solvability of the above problem is proposed. When this matrix inequality is feasible, an explicit expression for the desired state feedback controller is given. Furthermore, a numerical example is provided to demonstrate the effectiveness of the proposed approach. Copyright © 2002 John Wiley & Sons, Ltd.

56 citations

Journal ArticleDOI
TL;DR: The algorithm, which the authors call Symbolic HTM, is based on the organization of the harmonic transfer functions into a harmonic transfer matrix, which allows one to manipulate LPTV systems in a way that is similar to linear time-invariant systems.
Abstract: This paper presents an algorithm for generating symbolic expressions for the harmonic transfer functions of linear periodically time-varying (LPTV) systems, like mixers and PLLs. The harmonic transfer functions characterize the up- and downconversion behavior of the wanted and unwanted signal components. The algorithm, which the authors call Symbolic HTM, is based on the organization of the harmonic transfer functions into a harmonic transfer matrix. This representation allows one to manipulate LPTV systems in a way that is similar to linear time-invariant systems, making it possible to generate symbolic expressions relating the overall harmonic transfer functions to the building block parameters. These expressions can be used as design equations or as parameterized models for use in simulations or synthesis. Comparison of the symbolic models with numerical data shows them to be accurate, even for small numbers of modeling terms.

56 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023351
2022810
2021329
2020421
2019461
2018493