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Identification Algorithms (identification + algorithms)

Distribution by Scientific Domains

Engineering	75%
Chemistry	25%

Selected Abstracts

Identification of cascaded systems with linear and quantized observations,

ASIAN JOURNAL OF CONTROL, Issue 1 2010
Le Yi Wang
Abstract This paper studies identification of systems that can be decomposed into cascaded subsystems. The benefits of using additional sensors for identifying subsystems are investigated in terms of identification accuracy and time complexity. Identification algorithms, input design, and time complexity are first developed for subsystems, under various sensor types and locations. Overall reduction in estimation errors and time complexity is then analyzed to understand optimal selection of sensor locations and impact of sensor types on identification accuracy and time complexity. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source]

Real-Time Control and Identification of a Thermal Process Based on Multiple-Modeling Approach

ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING, Issue 3-4 2005
A. Aminzadeh
This article presents implementation of Real-Time Control and Identification algorithms based on a Multiple-Modeling approach for an experimental thermal process. The thermal process is a nonlinear plant; therefore, based on variations of the input and disturbance, four local operating regimes are defined. The linear local ARMAX models are identified for different regimes and integrated into a NARMAX model by combining them via proper validity and interpolation functions. Results of modeling the plant with a single model and multiple models show superior performance of the Multiple-Modeling technique which is also more flexible. Moreover, the Real-Time Control of the plant with four locally designed controllers is addressed. The platform used for the Real-Time implementation is Matlab/Simulink/Real-Time-Workshop with Visual C++ and Watcom compilers using a DAQ interface. The Real-Time application of the global controller based on the Multiple-Model approach demonstrates excellent performance for this design when compared to a single PID controller. [source]

Recursive subspace identification based on instrumental variable unconstrained quadratic optimization

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 9-10 2004
G. Mercère
The problem of the recursive formulation of the MOESP class of subspace identification algorithms is considered and two novel instrumental variable approaches are introduced. The first one leads to an RLS-like implementation, the second to a gradient type iteration. The relative merits of both approaches are analysed and discussed, while simulation results are used to compare their performance with one of the existing techniques. Copyright © 2004 John Wiley & Sons, Ltd. [source]

Identification of dual-rate systems based on finite impulse response models

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 7 2004
Feng Ding
Abstract Two identification algorithms, a least squares and a correlation analysis based, are developed for dual-rate stochastic systems in which the output sampling period is an integer multiple of the input updating period. The basic idea is to use auxiliary FIR models to predict unmeasurable noise-free (true) outputs, and then use these and system inputs to identify parameters of underlying fast single-rate models. The simulation results indicate that the proposed algorithms are effective. Copyright © 2004 John Wiley & Sons, Ltd. [source]

Robust identification/invalidation in an LPV framework

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 3 2010
Fernando D. Bianchi
Abstract A robust linear parameter varying (LPV) identification/invalidation method is presented. Starting from a given initial model, the proposed method modifies it and produces an LPV model consistent with the assumed uncertainty/noise bounds and the experimental information. This procedure may complement existing nominal LPV identification algorithms, by adding the uncertainty and noise bounds which produces a set of models consistent with the experimental evidence. Unlike standard invalidation results, the proposed method allows the computation of the necessary changes to the initial model in order to place it within the consistency set. Similar to previous LPV identification procedures, the initial parameter dependency is fixed in advance, but here a methodology to modify this dependency is presented. In addition, all calculations are made on state-space matrices which simplifies further controller design computations. The application of the proposed method to the identification of nonlinear systems is also discussed. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Automated protein identification by tandem mass spectrometry: Issues and strategies

MASS SPECTROMETRY REVIEWS, Issue 2 2006
Patricia Hernandez
Abstract Protein identification by tandem mass spectrometry (MS/MS) is key to most proteomics projects and has been widely explored in bioinformatics research. Obtaining good and trustful identification results has important implications for biological and clinical work. Although well matured, automated software identification of proteins from MS/MS data still faces a number of obstacles due to the complexity of the proteome or procedural issues of mass spectrometry data acquisition. Expected or unexpected modifications of the peptide sequences, polymorphisms, errors in databases, missed or non-specific cleavages, unusual fragmentation patterns, and single MS/MS spectra of multiple peptides of the same m/z are so many pitfalls for identification algorithms. A lot of research work has been carried out in recent years that yielded new strategies to handle a number of these issues. Multiple MS/MS identification algorithms are now available or have been theoretically described. The difficulty resides in choosing the most adapted method for each type of spectra being identified. This review presents an overview of the state-of-the-art bioinformatics approaches to the identification of proteins by MS/MS to help the reader doing the spadework of finding the right tools among the many possibilities offered. © 2005 Wiley Periodicals, Inc. Mass Spec Rev 25:235,254, 2006 [source]

Augmenting real data with synthetic data: an application in assessing Radio-Isotope identification algorithms,

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 8 2009
T. Burr
Abstract The performance of Radio-Isotope IDentification (RIID) algorithms using gamma spectroscopy is increasingly becoming important. For example, sensors at locations that screen for illicit nuclear material rely on isotope identification to resolve innocent nuisance alarms arising from naturally occurring radioactive material. Recent data collections for RIID testing consist of repeat measurements for each of several scenarios to test RIID algorithms. Efficient allocation of measurement resources requires an appropriate number of repeats for each scenario. To help allocate measurement resources in such data collections for RIID algorithm testing, we consider using only a few real repeats per scenario. In order to reduce uncertainty in the estimated RIID algorithm performance for each scenario, the potential merit of augmenting these real repeats with realistic synthetic repeats is also considered. Our results suggest that for the scenarios and algorithms considered, approximately 10 real repeats augmented with simulated repeats will result in an estimate having comparable uncertainty to the estimate based on using 60 real repeats. Published in 2009 by John Wiley & Sons, Ltd. [source]

Robust Isolation Of Sensor Failures

ASIAN JOURNAL OF CONTROL, Issue 1 2003
R. Xu
ABSTRACT Sensor self-validity check is a critical step in system control and fault diagnostics. In this paper, a robust approach to isolate sensor failures is proposed. First, a residual model for a given system is built off-line and directly based on input-output measurement data. The residual model outputs are called "primary residuals" and are zero when there is no fault. Most conventional approaches to residual model generation are indirect, as they first require the determination of state-space or other models using standard system identification algorithms. Second, a new max-min design of structured residuals, which can maximize the sensitivity of structured residuals with respect to sensor failures, is proposed. Based on the structured residuals, one can then isolate the sensor failures. This design can also be done in an off-line manner. It is an optimization procedure that avoids local optimal solutions. Simulation and experimental results demonstrated the effectiveness of the proposed method. [source]