scholarly journals Solution of a time-varying Wiener filtering problem

1967 ◽  
Vol 3 (12) ◽  
pp. 562 ◽  
Author(s):  
B.D.O. Anderson ◽  
J.B. Moore
Keyword(s):  
Wiener Filtering ◽  
Time Varying ◽  
Filtering Problem

scholarly journals Finite-TimeH∞Filtering for Linear Continuous Time-Varying Systems with Uncertain Observations

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Huihong Zhao ◽  
Chenghui Zhang
Keyword(s):  
Quadratic Form ◽  
Continuous Time ◽  
Finite Time ◽  
Krein Space ◽  
State Space Model ◽  
Time Varying ◽  
Bounded Noise ◽  
Time Varying Systems ◽  
Kreĭn Space ◽  
Filtering Problem

This paper is concerned with the finite-timeH∞filtering problem for linear continuous time-varying systems with uncertain observations andℒ2-norm bounded noise. The design of finite-timeH∞filter is equivalent to the problem that a certain indefinite quadratic form has a minimum and the filter is such that the minimum is positive. The quadratic form is related to a Krein state-space model according to the Krein space linear estimation theory. By using the projection theory in Krein space, the finite-timeH∞filtering problem is solved. A numerical example is given to illustrate the performance of theH∞filter.

Time-varying wavelet estimation and deconvolution by kurtosis maximization

Geophysics ◽  
2008 ◽  
Vol 73 (2) ◽  
pp. V11-V18 ◽  
Author(s):  
Mirko van der Baan
Keyword(s):  
Field Measurements ◽  
Phase Changes ◽  
Data Driven ◽  
Wiener Filtering ◽  
Time Varying ◽  
Seismic Wavelet ◽  
Phase Rotation ◽  
Physical Laws ◽  
Kurtosis Maximization ◽  
Zero Phase

Phase mismatches sometimes occur between final processed sections and zero-phase synthetics based on well logs, despite best efforts for controlled-phase acquisition and processing. The latter are often based on deterministic corrections derived from field measurements and physical laws. A statistical analysis of the data can reveal whether a time-varying nonzero phase is present. This assumes that the data should be white with respect to all statistical orders after proper deterministic corrections have been applied. Kurtosis maximization by constant phase rotation is a statistical method that can reveal the phase of a seismic wavelet. It is robust enough to detect time-varying phase changes. Phase-only corrections can then be applied by means of a time-varying phase rotation. Alternatively, amplitude and phase deconvolution can be achieved using time-varying Wiener filtering. Time-varying wavelet extraction and deconvolution can also be used as a data-driven alternative to amplitude-only inverse-[Formula: see text] deconvolution.

To: “The Determination of Optimum Gate Lengths for Time‐Varying Wiener Filtering” by Richard Wang, October 1969, p. 683–695

Geophysics ◽  
1970 ◽  
Vol 35 (1) ◽  
pp. 190-190
Keyword(s):  
Wiener Filtering ◽  
Time Varying

In the article “The Determination of Optimum Gate Lengths for Time‐Varying Wiener Filtering” by Richard Wang, October 1969, p. 683–695, the following corrections should be made.

scholarly journals A Novel Approach toℓ2-ℓ∞Filter Design for T-S Fuzzy Systems with Multiple Time-Varying Delays

2013 ◽  
Vol 2013 ◽  
pp. 1-11
Author(s):  
Xiaoyu Zhang ◽  
Cheng Gong ◽  
Zhen Zeng
Keyword(s):  
Fuzzy Systems ◽  
Filter Design ◽  
Disturbance Attenuation ◽  
Linear Matrix ◽  
Multiple Time ◽  
Time Varying ◽  
Weighting Matrix ◽  
Novel Approach ◽  
Delay Dependent ◽  
Filtering Problem

This paper investigates theℓ2-ℓ∞filtering problem of T-S fuzzy systems with multiple time-varying delays. First, by the Lyapunov-Krasovskii functional approach and free-weighting matrix method, a delay-dependent sufficient condition onℓ2-ℓ∞-disturbance attenuation is presented, in which both stability and prescribedℓ2-ℓ∞performance are required to be achieved for the filtering-error systems. Then, based on the condition, the full-order and reduced-order delay-dependentℓ2-ℓ∞filter design schemes for T-S fuzzy multiple time-varying delays systems are developed in terms of linear matrix inequality (LMI). Finally, an example is given to illustrate the effectiveness of the result.

scholarly journals Improved Generalized H2 Filtering for Static Neural Networks with Time-Varying Delay via Free-Matrix-Based Integral Inequality

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Hui-Jun Yu ◽  
Yong He ◽  
Min Wu
Keyword(s):  
Neural Networks ◽  
Integral Inequality ◽  
Time Varying ◽  
Globally Asymptotically Stable ◽  
Time Varying Delay ◽  
Static Neural Networks ◽  
Error System ◽  
Delay Dependent ◽  
Varying Delay ◽  
Filtering Problem

This paper focuses on the generalized H2 filtering of static neural networks with a time-varying delay. The aim of this problem is to design a full-order filter such that the filtering error system is globally asymptotically stable with guaranteed H2 performance index. By constructing an augmented Lyapunov-Krasovskii functional and applying the free-matrix-based integral inequality to estimate its derivative, an improved delay-dependent condition for the generalized H2 filtering problem is established in terms of LMIs. Finally, a numerical example is presented to show the effectiveness of the proposed method.

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