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    Wyświetlanie 1-7 z 7
    Tytuł:
    Analytic Interpolation and the Degree Constraint
    Autorzy:
    Georgiou, T. T.
    Powiązania:
    https://bibliotekanauki.pl/articles/908329.pdf
    Data publikacji:
    2001
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    interpolacja analityczna
    szereg czasowy
    analytic interpolation
    uniformly optimal control
    spectral analysis of time-series
    Opis:
    Analytic interpolation problems arise quite naturally in a variety of engineering applications. This is due to the fact that analyticity of a (transfer) function relates to the stability of a corresponding dynamical system, while positive realness and contractiveness relate to passivity. On the other hand, the degree of an interpolant relates to the dimension of the pertinent system, and this motivates our interest in constraining the degree of interpolants. The purpose of the present paper is to make an overview of recent developments on the subject as well as to highlight an application of the theory.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2001, 11, 1; 271-279
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Times series averaging and denoising from a probabilistic perspective on time-elastic kernels
    Autorzy:
    Marteau, Pierre-Francois
    Powiązania:
    https://bibliotekanauki.pl/articles/330311.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    time series averaging
    time elastic kernel
    dynamic time warping
    hidden Markov model
    szereg czasowy
    dynamiczne dopasowanie czasu
    ukryty model Markowa
    Opis:
    In the light of regularized dynamic time warping kernels, this paper re-considers the concept of a time elastic centroid for a set of time series. We derive a new algorithm based on a probabilistic interpretation of kernel alignment matrices. This algorithm expresses the averaging process in terms of stochastic alignment automata. It uses an iterative agglomerative heuristic method for averaging the aligned samples, while also averaging the times of their occurrence. By comparing classification accuracies for 45 heterogeneous time series data sets obtained by first nearest centroid/medoid classifiers, we show that (i) centroid-based approaches significantly outperform medoid-based ones, (ii) for the data sets considered, our algorithm, which combines averaging in the sample space and along the time axes, emerges as the most significantly robust model for time-elastic averaging with a promising noise reduction capability. We also demonstrate its benefit in an isolated gesture recognition experiment and its ability to significantly reduce the size of training instance sets. Finally, we highlight its denoising capability using demonstrative synthetic data. Specifically, we show that it is possible to retrieve, from few noisy instances, a signal whose components are scattered in a wide spectral band.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2019, 29, 2; 375-392
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Improving prediction models applied in systems monitoring natural hazards and machinery
    Autorzy:
    Sikora, M.
    Sikora, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/331302.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    zagrożenie naturalne
    szereg czasowy
    k-najbliższy sąsiad
    natural hazards monitoring
    regression rules
    time series forecasting
    k-nearest neighbors
    Opis:
    A method of combining three analytic techniques including regression rule induction, the k-nearest neighbors method and time series forecasting by means of the ARIMA methodology is presented. A decrease in the forecasting error while solving problems that concern natural hazards and machinery monitoring in coal mines was the main objective of the combined application of these techniques. The M5 algorithm was applied as a basic method of developing prediction models. In spite of an intensive development of regression rule induction algorithms and fuzzy-neural systems, the M5 algorithm is still characterized by the generalization ability and unbeatable time of data model creation competitive with other systems. In the paper, two solutions designed to decrease the mean square error of the obtained rules are presented. One consists in introducing into a set of conditional variables the so-called meta-variable (an analogy to constructive induction) whose values are determined by an autoregressive or the ARIMA model. The other shows that limitation of a data set on which the M5 algorithm operates by the k-nearest neighbor method can also lead to error decreasing. Moreover, three application examples of the presented solutions for data collected by systems of natural hazards and machinery monitoring in coal mines are described. In Appendix, results of several benchmark data sets analyses are given as a supplement of the presented results.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 2; 477-491
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An algorithm for arbitrary-order cumulant tensor calculation in a sliding window of data streams
    Autorzy:
    Domino, Krzysztof
    Gawron, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/330468.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    high order cumulant
    time series statistics
    nonnormally distributed data
    data streaming
    kumulant wysokiego rzędu
    szereg czasowy
    baza danych rozproszona
    strumień danych
    Opis:
    High-order cumulant tensors carry information about statistics of non-normally distributed multivariate data. In this work we present a new efficient algorithm for calculation of cumulants of arbitrary orders in a sliding window for data streams. We show that this algorithm offers substantial speedups of cumulant updates compared with the current solutions. The proposed algorithm can be used for processing on-line high-frequency multivariate data and can find applications, e.g., in on-line signal filtering and classification of data streams. To present an application of this algorithm, we propose an estimator of non-Gaussianity of a data stream based on the norms of high order cumulant tensors. We show how to detect the transition from Gaussian distributed data to non-Gaussian ones in a data stream. In order to achieve high implementation efficiency of operations on super-symmetric tensors, such as cumulant tensors, we employ a block structure to store and calculate only one hyper-pyramid part of such tensors.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2019, 29, 1; 195-206
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multisine Approximation of Multivariate Orthogonal Random Processes
    Autorzy:
    Figwer, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/908295.pdf
    Data publikacji:
    1999
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    proces stochastyczny
    procesy wielowymarowe
    transformata Fouriera
    simulation random processes
    multivariate orthogonal random processes
    simulated indentification
    multisine random time-series
    fast Fourier transform
    Opis:
    An approach to the synthesis and simulation of wide-sense stationary multivariate orthogonal random processes defined by their power spectral density matrices is presented. The approach is based on approximating the non-parametric power spectral density representation by the periodogram matrix of a multivariate orthogonal multisine random time-series. This periodogram matrix is used to construct the corresponding spectrum of the multivariate orthogonal multisine random time-series (synthesis). Application of the inverse finite discrete Fourier transform to this spectrum results in a multivariate orthogonal multisine random time-series with the predefined periodogram matrix (simulation). The properties of multivariate orthogonal multisine random process approximations obtained in this way are discussed. Attention is paid to asymptotic gaussianess.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 1999, 9, 2; 401-419
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Forecasting models for chaotic fractional-order oscillators using neural networks
    Autorzy:
    Bingi, Kishore
    Prusty, B Rajanarayan
    Powiązania:
    https://bibliotekanauki.pl/articles/2055150.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    chaotic oscillators
    data driven forecasting
    fractional order system
    model free analysis
    neural network
    time series prediction
    oscylator chaotyczny
    układ rzędu ułamkowego
    sieć neuronowa
    prognozowanie szeregów czasowych
    Opis:
    This paper proposes novel forecasting models for fractional-order chaotic oscillators, such as Duffing’s, Van der Pol’s, Tamaševičius’s and Chua’s, using feedforward neural networks. The models predict a change in the state values which bears a weighted relationship with the oscillator states. Such an arrangement is a suitable candidate model for out-of-sample forecasting of system states. The proposed neural network-assisted weighted model is applied to the above oscillators. The improved out-of-sample forecasting results of the proposed modeling strategy compared with the literature are comprehensively analyzed. The proposed models corresponding to the optimal weights result in the least mean square error (MSE) for all the system states. Further, the MSE for the proposed model is less in most of the oscillators compared with the one reported in the literature. The proposed prediction model’s out-of-sample forecasting plots show the best tracking ability to approximate future state values.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 3; 387--398
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence
    Autorzy:
    Li, C.
    Chiang, T. W.
    Powiązania:
    https://bibliotekanauki.pl/articles/331280.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    zbiór rozmyty
    system neuronowo-rozmyty
    optymalizacja rojem cząstek
    szereg czasowy
    complex fuzzy set
    complex neuro fuzzy system
    hierarchical multi swarm
    particle swarm optimization (PSO)
    recursive least squares estimator
    time series forecasting
    Opis:
    Financial investors often face an urgent need to predict the future. Accurate forecasting may allow investors to be aware of changes in financial markets in the future, so that they can reduce the risk of investment. In this paper, we present an intelligent computing paradigm, called the Complex Neuro-Fuzzy System (CNFS), applied to the problem of financial time series forecasting. The CNFS is an adaptive system, which is designed using Complex Fuzzy Sets (CFSs) whose membership functions are complex-valued and characterized within the unit disc of the complex plane. The application of CFSs to the CNFS can augment the adaptive capability of nonlinear functional mapping, which is valuable for nonlinear forecasting. Moreover, to optimize the CNFS for accurate forecasting, we devised a new hybrid learning method, called the HMSPSO-RLSE, which integrates in a hybrid way the so-called Hierarchical Multi-Swarm PSO (HMSPSO) and the well known Recursive Least Squares Estimator (RLSE). Three examples of financial time series are used to test the proposed approach, whose experimental results outperform those of other methods.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 787-800
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-7 z 7

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