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INITIAL-CONDITION ROBUSTNESS OF LINEAR LEAST-SQUARES filtering algorithms

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 1974年第4期AC19卷 393-397页

作者： AASNAES, HB KAILATH, T AS INFORMASJONSKONTROLL ASKERNORWAY STANFORD UNIV DEPT ELECT ENGNSTANFORDCA 94305

We give some sufficient conditions under which the mean-square error of linear least squares (lls) estimates converges to its true steady-state value despite perturbations due to uncertainties in initial conditions, round-off errors in calculation, etc. For state-variable estimators, this property, called initial-condition robustness, is implied by the exponential asymptotic stability of the estimating filter, but this latter property though desirable is of course far from necessary for the more basic (since mean-square error is the ultimate criterion) property of robustness. We present a general sufficient condition for such robustness of lls predictors of stochastic processes. This condition is then specialized to lls estimators for processes described by state-variable models and by autoregressive-moving agerage difference equation models. It is shown that our conditions can establish robustness in cases where previous criteria either fail or are inconclusive.

关键词： Robustness Least squares methods filtering algorithms State estimation Sufficient conditions Robust stability Mean square error methods Least squares approximation Steady-state Uncertainty

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Probabilistic Bounds for a Class of filtering algorithms in the Scalar Case

Probabilistic Bounds for a Class of Filtering Algorithms in ...

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American Control Conference (ACC)

作者： Wei, Shihong Spall, James C. Johns Hopkins Univ Appl Math & Stat Dept Whitehead Hall3400 North Charles St Baltimore MD 21218 USA JHU Appl Phys Lab Whitehead Hall3400 North Charles St Baltimore MD 21218 USA

ISBN: (纸本)9781665441971

We focus on the estimation error of a type of filtering algorithm in the scalar case, which is applicable to both linear and nonlinear systems. Under some regularity conditions, we construct a surrogate process that has a moment dominance property with respect to the true filtering error process. Then, moment-based probability inequalities can be used to compute probabilistic bounds for the filtering error. The sharpness of the bounds is tested on a simulated epidemic model with both Gaussian and non-Gaussian noise.

关键词： Estimation error Epidemics filtering Computational modeling filtering algorithms Probabilistic logic Nonlinear systems

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Beat tracking with particle filtering algorithms

Beat tracking with particle filtering algorithms

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IEEE Workshop on Applications of Signal Processing to Audio and Acoustics

作者： Hainsworth, S Macleod, M Univ Cambridge Dept Engn Cambridge CB2 1PZ England

This paper presents a beat tracking algorithm for musical audio signals. The method firstly extracts musical changepoints from the help signal and then uses a particle filtering algorithm to associate these to a tempo... 详细信息

ISBN: (纸本)0780378504

关键词： Application software Event detection filtering algorithms Foot Frequency estimation Humans Particle tracking Phase estimation Power harmonic filters Signal processing

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filtering algorithms for processing prefiltered measurements

Filtering algorithms for processing prefiltered measurements

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IEEE Conference on Decision and Control

作者： Anthony W. Warren Boeing Computer Services Inc. Seattle WA USA

The filtering algorithm for processing integral measurements given in reference [1] is extended to include analog type prefilters with infinite memory, and to include sequential processing of prefiltered data in multi-sensor, multi-sample rate systems. The extension to infinite memory prefilters requires serial decorrelation of the prefilter output prior to state estimation. These algorithms provide a technique for investigation of prefilter parameter sensitivities, and provide optimal compensation for any information loss due to prefiltering.

关键词： filtering algorithms State estimation Decorrelation Nonlinear filters Low-frequency noise Data processing Sensor systems Frequency IIR filters Band pass filters

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filtering algorithms for sequencing constraints: Application in physical mapping

Filtering algorithms for sequencing constraints: Application...

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ACS/IEEE International Conference on Computer Systems and Applications

作者： Hamza Zidoum Department of Computer Science Sultan Qaboos University-College of Science Oman

The physical mapping is a crucial tool in the analysis of the genomic sequences. algorithms for the mapping process are based on NP-complete combinatorial optimizations. The problem of reconstructing the probe order is equivalent to the consecutive ones problem. PQ-trees have been extensively used as a suitable data structure to test the consecutive ones property (COP) in the hybridization matrix. This paper presents PQR-trees, an extension of PQ-trees. PQR-trees can advantageously handle partial order information on probes. Moreover, we embed PQR-trees in the more general framework of Constraint Programming (CP). CP is an emergent software technology for declarative description and effective solving of large, particularly combinatorial, problems. We introduce Sequences a new data structure in CP and present filtering algorithms for checking the consistency of sequence constraints based on PQR-trees. We present a canonical form that characterizes a family of sequential arrangements of a given set. The relations we are dealing with are classical sets relations isin, sub, ne, = besides sequencing relations such as group, order, and metric constraints. The filtering algorithms are based on incremental consistency techniques used to reduce the PQR-trees and hence, prune the inconsistencies before the labeling phase. We claim that the sequence structure introduces a flexibility criterion on CP which renders it a suitable tool for solving NP-complete combinatorial optimizations such as physical mapping problem.

关键词： filtering algorithms Probes Cloning Sequences DNA Genomics Bioinformatics Data structures Labeling Application software

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filtering algorithms for speech signals in MAxk TLAB

Filtering algorithms for speech signals in MAxk TLAB

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International Conference on Information Science and Communications Technologies (ICISCT)

作者： Fakhriddin Abdirazakov Sukhrob Atoev Baydullaev Ruslan Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9781665432597

Typically, speech signals interfere with other types of signals. In cases where noise performance is enhanced, its continuation can be modified, analyzed, or the results of speech evaluation changed. In other cases, such as the analysis of noisy recordings for forensic purposes or the restoration of audio recordings in archives, the task of filtering the signal from noise are the main purpose of the research work. Therefore, the development of methods to filter the signal from noise is a very topical area of research.

关键词： Information science Speech coding filtering Forensics filtering algorithms Speech enhancement Probability distribution

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Systematic Evaluation of Protein Sequence filtering algorithms for Proteoform Identification Using Top-Down Mass Spectrometry

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PROTEOMICS 2018年第3-4期18卷

作者： Kou, Qiang Wu, Si Liu, Xiaowen Indiana Univ Purdue Univ Dept BioHlth Informat Indianapolis IN 46202 USA Univ Oklahoma Dept Chem & Biochem Norman OK 73019 USA Indiana Univ Sch Med Ctr Computat Biol & Bioinformat Indianapolis IN 46202 USA

Complex proteoforms contain various primary structural alterations resulting from variations in genes, RNA, and proteins. Top-down mass spectrometry is commonly used for analyzing complex proteoforms because it provides whole sequence information of the proteoforms. Proteoform identification by top-down mass spectral database search is a challenging computational problem because the types and/or locations of some alterations in target proteoforms are in general unknown. Although spectral alignment and mass graph alignment algorithms have been proposed for identifying proteoforms with unknown alterations, they are extremely slow to align millions of spectra against tens of thousands of protein sequences in high throughput proteome level analyses. Many software tools in this area combine efficient protein sequence filtering algorithms and spectral alignment algorithms to speed up database search. As a result, the performance of these tools heavily relies on the sensitivity and efficiency of their filtering algorithms. Here, we propose two efficient approximate spectrum-based filtering algorithms for proteoform identification. We evaluated the performances of the proposed algorithms and four existing ones on simulated and real top-down mass spectrometry data sets. Experiments showed that the proposed algorithms outperformed the existing ones for complex proteoform identification. In addition, combining the proposed filtering algorithms and mass graph alignment algorithms identified many proteoforms missed by ProSightPC in proteome-level proteoform analyses.

关键词： filtering algorithms spectral identification top-down mass spectrometry

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Adaptive filtering algorithms

Adaptive filtering algorithms

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IEEE Instrumentation and Measurement Technology Conference

作者： S. Theodoridis Department of Informatics and Telecommunications University of Athens (NKUA) Athens Greece

ISBN: (纸本)0780366468

System identification (SI) is the task of specifying an unknown system's model in terms of the available experimental evidence, that is a set of input-desired output response signal samples. System identification is a central issue in a large number of application areas, such as control, channel equalization, echo cancellation. This state-of-the-art article focuses on systems that can be modeled in terms of a Finite Impulse Response (FIR) and its goal is to present the available palette of adaptive SI algorithms in a unifying way.

关键词： Adaptive filters filtering algorithms Cost function Finite impulse response filter Wiener filter Approximation algorithms Stochastic processes System identification Least squares approximation Iterative algorithms

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Incremental filtering algorithms for Precedence and Dependency Constraints

Incremental Filtering Algorithms for Precedence and Dependen...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Roman Bartak Ondrej Cepek Faculty of Mathematics and Physics Charles University Prague Czech Republic

Precedence constraints play a crucial role in planning and scheduling problems. Many real-life problems also include dependency constraints expressing logical relations between the activities - for example, an activity requires presence of another activity in the plan. For such problems a typical objective is a maximization of the number of activities satisfying the precedence and dependency constraints. In the paper we propose new incremental filtering rules integrating propagation through both precedence and dependency constraints. We also propose a new filtering rule using the information about the requested number of activities in the plan. We demonstrate efficiency of the proposed rules on the log-based reconciliation problems and min-cutset problems

关键词： filtering algorithms Job shop scheduling Information filtering Information filters Mathematics Physics Electronic mail Acoustic propagation Virtual manufacturing Artificial intelligence

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Non-linear kalman filtering algorithms for on-line calibration of dynamic traffic assignment models

Non-linear kalman filtering algorithms for on-line calibrati...

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International Conference on Intelligent Transportation

作者： C. Antoniou M. Ben-Akiva H.N. Koutsopoulos Department of Transportation Planning and Engineering 5 Iroon Polytecniou National and Technical University of Athens Zografou Greece Department of Civil and Environmental Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Civil Engineering Northeastern University Boston MA USA

The problem of on-line calibration of dynamic traffic assignment (DTA) models is receiving increasing attention from researchers and practitioners. The problem can be formulated as a non-linear state-space model. Because of its nonlinear nature, the resulting model cannot be solved by the Kalman filter and therefore non-linear extensions need to be considered. In this paper, three extensions to the Kalman filter algorithm are presented: extended Kalman filter (EKF), limiting EKF (LimEKF), and unscented Kalman filter (UKF). The solution algorithms are applied to the calibration of the state-of-the-art DynaMIT-R DTA model and their use is demonstrated in a freeway network in Southampton, U.K. The LimEKF shows accuracy comparable to that of the best algorithm, but vastly superior computational performance

关键词： Kalman filters filtering algorithms Calibration Traffic control Predictive models Computational modeling Telecommunication traffic Data engineering State estimation Transportation

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