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Search results for: IDENTIFICATION OF NONSTATIONARY PROCESSES, BASISFUNCTION ESTIMATORS, ADAPTIVE ESTIMATION
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Fast Basis Function Estimators for Identification of Nonstationary Stochastic Processes
PublicationThe problem of identification of a linear nonsta-tionary stochastic process is considered and solved using theapproach based on functional series approximation of time-varying parameter trajectories. The proposed fast basis func-tion estimators are computationally attractive and yield resultsthat are better than those provided by the local least squaresalgorithms. It is shown that two...
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Identification of nonstationary multivariate autoregressive processes– Comparison of competitive and collaborative strategies for joint selection of estimation bandwidth and model order
PublicationThe problem of identification of multivariate autoregressive processes (systems or signals) with unknown and possibly time-varying model order and time-varying rate of parameter variation is considered and solved using parallel estimation approach. Under this approach, several local estimation algorithms, with different order and bandwidth settings, are run simultaneously and compared based on their predictive performance. First,...
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Regularized Local Basis Function Approach to Identification of Nonstationary Processes
PublicationThe problem of identification of nonstationary stochastic processes (systems or signals) is considered and a new class of identification algorithms, combining the basis functions approach with local estimation technique, is described. Unlike the classical basis function estimation schemes, the proposed regularized local basis function estimators are not used to obtain interval approximations of the parameter trajectory, but provide...
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Generalized Savitzky–Golay filters for identification of nonstationary systems
PublicationThe problem of identification of nonstationary systems using noncausal estimation schemes is consid-ered and a new class of identification algorithms, combining the basis functions approach with localestimationtechnique,isdescribed.Unliketheclassicalbasisfunctionestimationschemes,theproposedlocal basis function estimators are not used to obtain interval approximations of the parametertrajectory, but provide a sequence of point...
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New results on estimation bandwidth adaptation
PublicationThe problem of identification of a nonstationary autoregressive signal using non-causal estimation schemes is considered. Noncausal estimators can be used in applications that are not time-critical, i.e., do not require real-time processing. A new adaptive estimation bandwidth selection rule based on evaluation of pseudoprediction errors is proposed, allowing one to adjust tracking characteristics of noncausal estimators to unknown...
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Local basis function estimators for identification of nonstationary systems
PublicationThe problem of identification of a nonstationary stochastic system is considered and solved using local basis function approximation of system parameter trajectories. Unlike the classical basis function approach, which yields parameter estimates in the entire analysis interval, the proposed new identification procedure is operated in a sliding window mode and provides a sequence of point (rather than interval) estimates. It is...
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On Noncausal Identification of Nonstationary Multivariate Autoregressive Processes
PublicationThe problem of identification of nonstationary multivariate autoregressive processes using noncausal local estimation schemes is considered and a new approach to joint selection of the model order and the estimation bandwidth is proposed. The new selection rule, based on evaluation of pseudoprediction errors, is compared with the previously proposed one, based on the modified Akaike’s final prediction error criterion.
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Identification of nonstationary processes using noncausal bidirectional lattice filtering
PublicationThe problem of off-line identification of a nonstationary autoregressive process with a time-varying order and a time-varying degree of nonstationarity is considered and solved using the parallel estimation approach. The proposed parallel estimation scheme is made up of several bidirectional (noncausal) exponentially weighted lattice algorithms with different estimation memory and order settings. It is shown that optimization of...
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On adaptive covariance and spectrum estimation of locally stationary multivariate processes
PublicationWhen estimating the correlation/spectral structure of a locally stationary process, one has to make two important decisions. First, one should choose the so-called estimation bandwidth, inversely proportional to the effective width of the local analysis window, in the way that complies with the degree of signal nonstationarity. Too small bandwidth may result in an excessive estimation bias, while too large bandwidth may cause excessive...
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On joint order and bandwidth selection for identification of nonstationary autoregressive processes
PublicationWhen identifying a nonstationary autoregressive process, e.g. for the purpose of signal prediction or parametric spectrum estimation, two important decisions must be taken. First, one should choose the appropriate order of the autoregressive model, i.e., the number of autoregressive coefficients that will be estimated. Second, if identification is carried out using the local estimation technique, such as the localized version of...
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Locally Adaptive Cooperative Kalman Smoothing and Its Application to Identification of Nonstationary Stochastic Systems
PublicationOne of the central problems of the stochastic approximation theory is the proper adjustment of the smoothing algorithm to the unknown, and possibly time-varying, rate and mode of variation of the estimated signals/parameters. In this paper we propose a novel locally adaptive parallel estimation scheme which can be used to solve the problem of fixed-interval Kalman smoothing in the presence of model uncertainty. The proposed solution...
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Towards Robust Identification of Nonstationary Systems
PublicationThe article proposes a fast, two-stage method for the identification of nonstationary systems. The method uses iterative reweighting to robustify the identification process against the outliers in the measurement noise and against the numerical errors that may occur at the first stage of identification. We also propose an adaptive algorithm to optimize the values of the hyperparameters that are crucial for this new method.
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Estimators of covariance matrices in Msplit(q) estimation
PublicationThis paper proposes methods for the determination of covariance matrices of Msplit(q) estimators. The solutions presented here allow Msplit(q) estimation to be supplemented by the operations from the domain of accuracy analysis (especially that concerning estimators of parameters). Theoretical forms of covariance matrices of Msplit(q) estimators were established using the empirical influence functions and the equivalent covariance...
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On noncausal identification of nonstationary stochastic systems
PublicationIn this paper we consider the problem of noncausal identification of nonstationary,linear stochastic systems, i.e., identification based on prerecorded input/output data. We show how several competing weighted least squares parameter smoothers, differing in memory settings, can be combined together to yield a better and more reliable smoothing algorithm. The resulting parallel estimation scheme automatically adjusts its smoothing...
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Zdzisław Kowalczuk prof. dr hab. inż.
PeopleZdzislaw Kowalczuk received his M.Sc. degree in 1978 and Ph.D. degree in 1986, both in Automatic Control from Technical University of Gdańsk (TUG), Gdańsk, Poland. In 1993 he received his D.Sc. degree (Dr Habilitus) in Automatic Control from Silesian Technical University, Gliwice, Poland, and the title of Professor from the President of Poland in 2003. Since 1978 he has been with Faculty of Electronics, Telecommunications and Informatics...
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Two-Stage Identification of Locally Stationary Autoregressive Processes and its Application to the Parametric Spectrum Estimation
PublicationThe problem of identification of a nonstationary autoregressive process with unknown, and possibly time-varying, rate of parameter changes, is considered and solved using the parallel estimation approach. The proposed two-stage estimation scheme, which combines the local estimation approach with the basis function one, offers both quantitative and qualitative improvements compared with the currently used single-stage methods.
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On the preestimation technique and its application to identification of nonstationary systems
PublicationThe problem of noncausal identification of a nonstationary stochastic FIR (finite impulse response) sys- tem is reformulated, and solved, as a problem of smoothing of preestimated parameter trajectories. Three approaches to preestimation are critically analyzed and compared. It is shown that optimization of the smoothing operation can be performed adaptively using the parallel estimation technique. The new approach is computationally...
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On Adaptive Spectrum Estimation of Multivariate Autoregressive Locally Stationary Processes
PublicationAutoregressive modeling is a widespread parametricspectrum estimation method. It is well known that, in the caseof stationary processes with unknown order, its accuracy canbe improved by averaging models of different complexity usingsuitably chosen weights. The paper proposes an extension of thistechnique to the case of multivariate locally stationary processes.The proposed solution is based on local autoregressive...
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On adaptive selection of estimation bandwidth for analysis of locally stationary multivariate processes
PublicationWhen estimating the correlation/spectral structure of a locally stationary process, one should choose the so-called estimation bandwidth, related to the effective width of the local analysis window. The choice should comply with the degree of signal nonstationarity. Too small bandwidth may result in an excessive estimation bias, while too large bandwidth may cause excessive estimation variance. The paper presents a novel method...
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Local basis function method for identification of nonstationary systems
PublicationThis thesis is focused on the basis function method for the identification of nonstationary processes. The first chapter describes a group of models that can be identified using the basis function method. The next chapter describes the basic version of the basis function method, including its algebraic and statistical properties. The following section introduces the local basis function (LBF) method: its properties are described...
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A new look at the statistical identification of nonstationary systems
PublicationThe paper presents a new, two-stage approach to identification of linear time-varying stochastic systems, based on the concepts of preestimation and postfiltering. The proposed preestimated parameter trajectories are unbiased but have large variability. Hence, to obtain reliable estimates of system parameters, the preestimated trajectories must be further filtered (postfiltered). It is shown how one can design and optimize such...
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On noncausal weighted least squares identification of nonstationary stochastic systems
PublicationIn this paper, we consider the problem of noncausal identification of nonstationary, linear stochastic systems, i.e., identification based on prerecorded input/output data. We show how several competing weighted (windowed) least squares parameter smoothers, differing in memory settings, can be combined together to yield a better and more reliable smoothing algorithm. The resulting parallel estimation scheme automatically adjusts...
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Locally-adaptive Kalman smoothing approach to identification of nonstationary stochastic systems
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Smooth least absolute deviation estimators for outlier-proof identification
PublicationThe paper proposes to identify the parameters of linear dynamic models based on the original implementation of least absolute deviation estimators. It is known that the object estimation procedures synthesized in the sense of the least sum of absolute prediction errors are particularly resistant to occasional outliers and gaps in the analyzed system data series, while the classical least squares procedure unfortunately becomes...
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Fast recursive basis function estimators for identification of time-varying processes
PublicationW pracy wprowadzono nową kategorię filtrów adaptacyjnych opartych na metodzie funkcji bazowych i wykorzystujących koncepcję postfiltracji. Proponowane algorytmy pozwalają połączyć niską złożoność obliczeniową i dobre właściwości śledzące.
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ESTIMATION OF NONSTATIONARY HARMONIC SIGNALS AND ITS APPLICATION TO ACTIVE CONTROL OF MRI NOISE
PublicationA new adaptive comb filtering algorithm, capable of tracking the fundamental frequency and amplitudes of different frequency components of a nonstationary harmonic signal embedded in white measurement noise, is proposed. Frequency tracking characteristics of the new scheme are studied analytically, proving (under Gaussian assumptions and optimal tuning) its statistical efficiency for quasi-linear frequency changes. Laboratory tests...
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Generalized adaptive comb filters/smoothers and their application to the identification of quasi-periodically varying systems and signals
PublicationThe problem of both causal and noncausal identification of linear stochastic systems with quasiharmonically varying parameters is considered. The quasi-harmonic description allows one to model nonsinusoidal quasi-periodic parameter changes. The proposed identification algorithms are called generalized adaptive comb filters/smoothers because in the special signal case they reduce down to adaptive comb algorithms used to enhance...
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Identification of Unstable Reference Points and Estimation of Displacements Using Squared Msplit Estimation
PublicationThe article presents a new version of the method for estimating parameters in a split functional model, which enables the determination of displacements of geodetic network points with constrained datum. The main aim of the study is to present theoretical foundations of Msplit CD estimation and its basic properties and possible applications. Particular attention was paid to the efficacy of the method in the context of geodetic...
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A simple way of increasing estimation accuracy of generalized adaptive notch filters
PublicationGeneralized adaptive notch filters are used for identification/tracking of quasi-periodically varying dynamic systems and can be considered an extension, to the system case, of classical adaptive notch filters. It is shown that frequency biases, which arisein generalized adaptive notch filtering algorithms, can be significantly reduced by incorporating in the adaptive loop an appropriately chosen decision delay. The resulting performance...
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New approach to noncausal identification of nonstationary stochastic systems subject to both smooth and abrupt parameter changes
PublicationIn this paper we consider the problem of finiteintervalparameter smoothing for a class of nonstationary linearstochastic systems subject to both smooth and abrupt parameterchanges. The proposed parallel estimation scheme combines theestimates yielded by several exponentially weighted basis functionalgorithms. The resulting smoother automatically adjustsits smoothing bandwidth to the type and rate of nonstationarityof the identified...
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New Approach to Noncasual Identification of Nonstationary Stochastic FIR Systems Subject to Both Smooth and Abrupt Parameter Changes
PublicationIn this technical note, we consider the problem of finite-interval parameter smoothing for a class of nonstationary linear stochastic systems subject to both smooth and abrupt parameter changes. The proposed parallel estimation scheme combines the estimates yielded by several exponentially weighted basis function algorithms. The resulting smoother automatically adjusts its smoothing bandwidth to the type and rate of nonstationarity...
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Asynchronous distributed state estimation for continuous-time stochastic processes
PublicationWe consider the problem of state estimation of a continuous-time stochastic process using an asynchronous distributed multi-sensor estimation system (ADES). In an ADES the state of a process of interest is estimated by a group of local estimators. Each local estimator based, for example, on a Kalman filter, performs single sensor filtration but also fusion of its local results and results from other (remote) processors to compute...
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Multiresolution analysis and adaptive estimation on a sphere using stereographic wavelets
PublicationWe construct an adaptive estimator of a density function on d dimensional unit sphere Sd (d ≥ 2), using a new type of spherical frames. The frames, or as we call them, stereografic wavelets are obtained by transforming a wavelet system, namely Daubechies, using some stereographic operators. We prove that our estimator achieves an optimal rate of convergence on some Besov type class of functions by adapting to unknown smoothness....
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Non-Adaptive Speed and Position Estimation of Doubly-Fed Induction Generator in Grid-Connected Operations
PublicationThe nonadaptive speed and position estimation scheme for a doubly-fed induction generator (DFIG) is presented in this article. The observer structure is based on the extension of the mathematical model of DFIG to the introduced H vector. Based on the defined H vector, the nonadaptive position and speed estimation is proposed. The Lyapunov method is extended to the practical stability theorem to stabilize the structure. The classic...
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Asynchronous Networked Estimation System for Continuous Time Stochastic Processes
PublicationIn this paper we examine an asynchronous networked estimation system for state estimation of continuous time stochastic processes. Such a system is comprised of several estimation nodes connected using a possibly incomplete communication graph. Each of the nodes uses a Kalman filter algorithm and data from a local sensor to compute local state estimates of the process under observation. It also performs data fusion of local estimates...
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ADAPTIVE IDENTIFICATION OF TIME-VARYING IMPULSE RESPONSE OF UNDERWATER ACOUSTIC COMMUNICATION CHANNEL
PublicationThe transmission properties of underwater acoustic communication channel can change dynamically due to the movement of acoustic system transmitter and receiver or underwater objects reflecting transmitted signal. The time-varying impulse response measurement and estimation are necessary to match the physical layer of data transmission to instantaneous channel propagation conditions. Using the correlative measurement method, impulse...
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Adaptive Identification of Underwater Acoustic Channel with a Mix of Static and Time-Varying Parameters
PublicationWe consider the problem of identification of communication channels with a mix of static and time-varying parameters. Such scenarios are typical, among others, in underwater acoustics. In this paper, we further develop adaptive algorithms built on the local basis function (LBF) principle resulting in excellent performance when identifying time-varying systems. The main drawback of an LBF algorithm is its high complexity. The subsequently...
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Artur Gańcza dr inż.
PeopleI received the M.Sc. degree from the Gdańsk University of Technology (GUT), Gdańsk, Poland, in 2019. I am currently a Ph.D. student at GUT, with the Department of Automatic Control, Faculty of Electronics, Telecommunications and Informatics. My professional interests include speech recognition, system identification, adaptive signal processing and linear algebra.
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Study on the time-frequency analysis of nonstationary, electrochemical and corrosion processes
PublicationW pracy przedstawiono możliwości zastosowania czasowo-częstotliwościowych metod analizy sygnałów do badania niestacjonarnych procesów elektrochemicznych, chemicznych i korozyjnych. Wnikliwej analizie z wykorzystaniem metod STFT, rozkładu Wignera-Ville'a oraz przekształceń z klasy Cohena poddano rejestry oscylacji chemicznych, elektrochemicznych i rejestry korozji wżerowej.
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Adaptive identification of sparse underwater acoustic channels with a mix of static and time-varying parameters
PublicationWe consider identification of sparse linear systems with a mix of static and time-varying parameters. Such systems are typical in underwater acoustics (UWA), for instance, in applications requiring identi- fication of the acoustic channel, such as UWA communications, navigation and continuous-wave sonar. The recently proposed fast local basis function (fLBF) algorithm provides high performance when identi- fying time-varying systems....
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On the concept of estimation memory in adaptive filtering.
PublicationArtykuł przedstawia i omawia pojęcie pamięci estymacji, pozwalające na obiektywne porównanie właściwości śledzących różnych algorytmów adaptacyjnych filtracji.
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Non-Adaptive Rotor Speed Estimation of Induction Machine in an Adaptive Full-Order Observer
PublicationIn the sensorless control system of an induction machine, the rotor speed value is not measured but reconstructed by an observer structure. The rotor speed value can be reconstructed by the classical adaptive law with the integrator. The second approach, which is the main contribution of this paper, is the non-adaptive structure without an integrator. The proposed method of the rotor speed reconstruction is based on an algebraic...
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Matrix Strengthening the Identification of Observations with Split Functional Models in the Squared Msplit(q) Estimation Process
PublicationThis article addresses the issue of raising the level of identification of observations with either single or more split functional models in the squared Msplit(q) estimation process. The theoretical part of the study presents the theoretical grounds for the classical method for estimating parameters in a split functional model and proposes a modification of the computational algorithm to increase the quality of the determinations...
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Optimal asynchronous estimation of 2D Gaussian-Markov processes
PublicationW artykule rozważa się problem estymacji trajektorii dwuwymiarowych ciągłoczasowych procesów Gaussa-Markowa na podstawie zaszumionych pomiarów wykonywanych w nierównomiernie rozłożonych chwilach czasu. W przypadku takiego problemu, w każdym cyklu pracy algorytmu należy dokonać dyskretnoczasowej predykcji (analogicznie jak w przypadku filtru Kalmana). Niestety zadanie to może być złożone obliczeniowo. Aby rozwiązać ten problem,...
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Identification of Non-stationary and Non-linear Drying Processes
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Estimation of Housing Demand with Adaptive Neuro-Fuzzy Inference Systems (ANFIS)
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Adaptive estimation of the transformer stray capacitances for DC–DC converter modelling
PublicationNew low cost and accurate estimation method of transformer stray capacitances for wide band DC–DC converter modelling and design is proposed. The Wiener filter (WF) method is applied to estimate the transformer impedance – referred to the selected transformer winding configurations. Laboratory tests are used to adapt the filter, that is to find optimal impedance which minimises mean square error between measured, noise perturbed...
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Estimation of Housing Demand with Adaptive Neuro-Fuzzy Inference Systems (ANFIS)
PublicationIt has always been important to anticipate the demand for a product. To determine the demand for any product, the parameters such as the economic situation and the demands of the rival products are used generally. Especially in the housing sector, which is the locomotive sector for emerging countries, it is critical to anticipate housing demand and its relationship with economic variables. Because of that, economists, real estate...
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Multi-objective optimization of tool wear, surface roughness, and material removal rate in finishing honing processes using adaptive neural fuzzy inference systems
PublicationHoning processes are usually employed to manufacture combustion engine cylinders and hydraulic cylinders. A crosshatch pattern is obtained that favors the oil flow. In this paper, Adaptive Neural Fuzzy Inference System (ANFIS) models were obtained for tool wear, average roughness Ra, cylindricity and material removal rate in finish honing processes. In addition, multi-objective optimization with the desirability function method...
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Nonadaptive estimation of the rotor speed in an adaptive full order observer of induction machine
PublicationThe article proposes a new method of reproducing the angular speed of the rotor of a cage induction machine designed for speed observers based on the adaptive method. In the proposed solution, the value of the angular speed of the rotor is not determined by the classical law of adaptation using the integrator only by an algebraic relationship. Theoretical considerations were confirmed by simulation and experimental tests.