A new approach to volterra-kernel estimation of bilinear systems

1978 ◽  
Author(s):  
R. Baheti ◽  
R. Mohler ◽  
H. Spang
Keyword(s):  
Kernel Estimation ◽  
Bilinear Systems ◽  
Volterra Kernel ◽  
New Approach

scholarly journals Regularized nonparametric Volterra kernel estimation

Automatica ◽  
2017 ◽  
Vol 82 ◽  
pp. 324-327 ◽  
Author(s):  
Georgios Birpoutsoukis ◽  
Anna Marconato ◽  
John Lataire ◽  
Johan Schoukens
Keyword(s):  
Kernel Estimation ◽  
Volterra Kernel

Volterra kernel estimation for mildly nonlinear amplifiers

1993 ◽  
Vol 29 (23) ◽  
pp. 2007 ◽  
Author(s):  
J.G. McRory ◽  
R. Johnston
Keyword(s):  
Kernel Estimation ◽  
Volterra Kernel ◽  
Nonlinear Amplifiers

scholarly journals A New Approach to Detection of Changes in Multidimensional Patterns

2020 ◽  
Vol 10 (2) ◽  
pp. 125-136 ◽  
Author(s):  
Tomasz Gałkowski ◽  
Adam Krzyżak ◽  
Zbigniew Filutowicz
Keyword(s):  
Kernel Estimation ◽  
Detection Algorithm ◽  
Heterogeneous Data ◽  
Data Types ◽  
New Approach ◽  
Abrupt Changes ◽  
Changes Detection ◽  
Data Collections ◽  
Derivatives Of

AbstractNowadays, unprecedented amounts of heterogeneous data collections are stored, processed and transmitted via the Internet. In data analysis one of the most important problems is to verify whether data observed or/and collected in time are genuine and stationary, i.e. the information sources did not change their characteristics. There is a variety of data types: texts, images, audio or video files or streams, metadata descriptions, thereby ordinary numbers. All of them changes in many ways. If the change happens the next question is what is the essence of this change and when and where the change has occurred. The main focus of this paper is detection of change and classification of its type. Many algorithms have been proposed to detect abnormalities and deviations in the data. In this paper we propose a new approach for abrupt changes detection based on the Parzen kernel estimation of the partial derivatives of the multivariate regression functions in presence of probabilistic noise. The proposed change detection algorithm is applied to oneand two-dimensional patterns to detect the abrupt changes.

Factors Affecting Volterra Kernel Estimation: Emphasis on Lung Tissue Viscoelasticity

10.1114/1.82 ◽  
1998 ◽  
Vol 26 (1) ◽  
pp. 103-116 ◽  
Author(s):  
Qin Zhang ◽  
Béla Suki ◽  
David T. Westwick ◽  
Kenneth R. Lutchen
Keyword(s):  
Lung Tissue ◽  
Kernel Estimation ◽  
Volterra Kernel ◽  
Factors Affecting

EM-Based Hyperparameter Optimization for Regularized Volterra Kernel Estimation

2017 ◽  
Vol 1 (2) ◽  
pp. 388-393 ◽  
Author(s):  
Jeremy G. Stoddard ◽  
James S. Welsh ◽  
Hakan Hjalmarsson
Keyword(s):  
Kernel Estimation ◽  
Volterra Kernel ◽  
Hyperparameter Optimization

scholarly journals Frequency Domain Kernel Estimation for 2nd-order Volterra Models Using Random Multi-tone Excitation

VLSI Design ◽  
2002 ◽  
Vol 15 (4) ◽  
pp. 701-713 ◽  
Author(s):  
G. Bicken ◽  
G. F. Carey ◽  
R. O. Stearman
Keyword(s):  
Frequency Domain ◽  
Semiconductor Device ◽  
Kernel Estimation ◽  
Harmonic Excitation ◽  
Volterra Kernel ◽  
Volterra Models ◽  
Output System ◽  
Gaussian Input ◽  
Kernel Estimations ◽  
Least Squares Minimization

We consider the problem of frequency domain kernel estimation using random multi-tone (harmonic) excitation for 2nd-order Volterra models. The basic approach is based on least squares minimization of model output error, and results for the Volterra kernel estimations with random multi-tone inputs and random Gaussian input are compared. We show that kernel estimation with multi-tones are very accurate and efficient compared to the latter. As an illustration, the proposed method is applied to a discrete input–output system obtained from the numerical simulation of a representative hydrodynamic system for modeling semiconductor device transport. We also consider the effect of noise in the kernel estimation.

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