TB2 Estimation and Identification

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Spectral Factorization by Means of Discrete Fourier Transform

Authors:

Jezek J., Academy of Sciences of the Czech Republic, Czech Republic

Hromcik M., Academy of Sciences of the Czech Republic, Czech Republic

Sebek M., Academy of Sciences of the Czech Republic, Czech Republic

ABSTRACT

A new algorithm is presented for the spectral factorization of a two- sided symmetric polynomial. This algorithm being combined with digonalization techniques for polynomial matrices can also be utilized in the multivariable case to calculate the spectral factor of a discrete-time para-Hermitian polynomial matrix. The proposed method is based on the discrete Fourier transform theory (DFT) and its relation to the Z-transform. Involving DFT into the computations brings high efficiency and reliability due to desirable numerical properties of the fast Fourier transform algorithm standing behind. The paper also includes an illustrative numerical example and discusses the numerical properties of the suggested routine with respect to other existing techniques.

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State Estimation with Uncertain Parametric Models

Authors:

Sayed A., UCLA, USA

ABSTRACT

This paper develops robust estimation algorithms for state-space models that are subject to bounded parametric uncertainties. Compared with existing robust filters, the new filters perform data regularization rather than de-regularization and they do not require existence conditions. The resulting filter structures also turn out to be similar to various (time- and measurement-update, prediction, and information) forms of the Kalman filter, albeit ones that operate on corrected parameters rather than on the given nominal parameters.

Keywords: Estimation, parametric uncertainty, set-valued estimation, Kalman filtering, Hoo filtering, guaranteed-cost design, steady-state filter, regularized least-squares.

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The Impact of Scheduled Maintenance on the failure process of Electric Rail Vehicles

Authors:

Stavropoulos Ch., University of Patras, Greece

Fassois S. - University of Patras, Greece

ABSTRACT

This paper addresses the stochastic modeling and impact assessment of scheduled maintenance actions on the reliability of electric rail vehicles, the latter expressed in terms of recorded Times Between Failures (TBFs). The study is based upon historical time series data from the Athens Electric Railways and intervention analysis within a novel non-stationary Functional Series modeling framework, which allows for the modeling, scheduled maintenance impact assessment, analysis, as well as failure time prediction. The results of the study indicate that intervention models incorporating scheduled maintenance effects are significantly better than their unaccounting counterparts. Furthermore, the statistical significance of the maintenance effects is demon- strated, and reliability prediction is shown to be feasible.

Key Words. Maintenance effects, stochastic reliability, failure process analysis, intervention analysis, non-stationary time series.

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Applications of Fuzzy Neural Networks with Nonlinear Consequences to System Identification

Authors:

Overstreet J., Polytechnic, USA

Tzes A., University of Patras, Greece

ABSTRACT

The objective of this article is to formulate a generic Fuzzy Logic Identifier (FLI) with a neural network structure for identification purposes of nonlinear systems. This FLI extends the current limited representation of fuzzy models by modifying its consequence part as a ratio of poly- nomials of the input variable. The weights of the premise and consequence parts are tuned in an adaptive manner based on the backpropagation algorithm. The suggested scheme is applied in identifying the nonlinear aspects of fric- tion in a dcmotor micromaneuvering system.

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Primitive Target Localization and Identification Using CTFM Sonar Imaging

Authors:

Politis Z., University of Oxford, United Kingdom

Probert P., University of Oxford, United Kingdom

ABSTRACT

In this paper we introduce a new method for acquiring and processing ultrasound signals for the location and identification of the typical primitives for map building - planes, obtuse angled corners, cylinders, right angled corners and edges. The transducer is a continuous wave single frequency modulated (CFTM) transmitter/receiver, not original in terms of its hardware but with little reported application in this field. Models of the echo signals received allows the feature parametrization of their range and amplitude values with respect to the distance and orientation of the target. Receiver saturation due to the large dynamic range is included in the models. Based on these models and the geometry of a re ection from plane and other targets we proved that only two measurements of the target at two distinctive positions are fulfill the minimum information requirement to localize and classify the studied targets. The proposed method was tested over a set of measurements from the five target types and the results strongly matched the theoretical implications. Applications of this method are suggested.

Keywords. Acoustic sensors, geometrical modeling, CTFM sonar, mobile robots.

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