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1
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A vehicle-track-soil dynamic interaction problem in sequential and parallel formulation

100%
Kogut J., Ciurej H.
International Journal of Applied Mathematics and Computer Science
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2010
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tom 20
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nr 2
295-303
EN
Some problems regarding numerical modeling of predicted vibrations excited by railway traffic are discussed. Model formulation in the field of structural mechanics comprises a vehicle, a track (often in a tunnel) and soil. Time consuming computations are needed to update large matrices at every discrete step. At first, a sequential Matlab code is generated. Later on, the formulation is modified to use grid computing, thereby a significant reduction in computational time is expected.
2
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A comparative and experimental study on gradient and genetic optimization algorithms for parameter identification of linear MIMO models of a drilling vessel

100%
Bańka S., Brasel M., Dworak P., Jaroszewski K.
International Journal of Applied Mathematics and Computer Science
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2015
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tom 25
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nr 4
877-893
EN
The paper presents algorithms for parameter identification of linear vessel models being in force for the current operating point of a ship. Advantages and disadvantages of gradient and genetic algorithms in identifying the model parameters are discussed. The study is supported by presentation of identification results for a nonlinear model of a drilling vessel.
3
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Identification of parametric models with a priori knowledge of process properties

100%
Janiszowski K., Wnuk P.
International Journal of Applied Mathematics and Computer Science
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2016
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tom 26
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nr 4
767-776
EN
An approach to estimation of a parametric discrete-time model of a process in the case of some a priori knowledge of the investigated process properties is presented. The knowledge of plant properties is introduced in the form of linear bounds, which can be determined for the coefficient vector of the parametric model studied. The approach yields special biased estimation of model coefficients that preserves demanded properties. A formula for estimation of the model coefficients is derived and combined with a recursive scheme determined for minimization of the sum of absolute model errors. The estimation problem of a model with known static gains of inputs is discussed and proper formulas are derived. This approach can overcome the non-identifiability problem which has been observed during estimation based on measurements recorded in industrial closed-loop control systems. The application of the proposed approach to estimation of a model for an industrial plant (a water injector into the steam flow in a power plant) is presented and discussed.
4
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The steady-state impedance operator of a Linear Periodically Time-Varying one-port network and its determination

75%
Kłosiński R.
International Journal of Applied Mathematics and Computer Science
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2009
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tom 19
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nr 4
661-673
EN
The main subject of the paper is the description and determination of the impedance operator of a linear periodically timevarying (LPTV) one-port network in the steady-state. If the one-port network parameters and the supply vary periodically with the same period, the network reaches a periodic steady state. However, the sinusoidal supply may induce a nonsinusoidal voltage or current. It is impossible to describe such a phenomenon by means of one complex number. A periodically time-varying one-port network working in a steady-state regime can be described with a circular parametric operator. Within the domain of discrete time, such an operator takes the form of a matrix with real-valued entries. The circular parametric operator can be transformed into the frequency domain using a two-dimensional DFT. This description makes it possible to quantitatively assess LPTV system input and output harmonics aliasing. The paper also presents the derivation and the proof of convergence of an iteration scheme for the identification of circular parametric operators. The scheme may be used to determine the impedance of an LPTV one-port network. Some results of computer simulations are shown.
5
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Modulating element method in the identification of a generalized dynamical system

75%
Wysocki H., Zellma M.
Applicationes Mathematicae
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1993-1995
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tom 22
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nr 4
447-467
EN
In this paper the identification of generalized linear dynamical differential systems by the method of modulating elements is presented. The dynamical system is described in the Bittner operational calculus by an abstract linear differential equation with constant coefficients. The presented general method can be used in the identification of stationary continuous dynamical systems with compensating parameters and for certain nonstationary compensating or distributed parameter systems.
6
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Neural network based identification of hysteresis in human meridian systems

75%
Tan Y., Dong R., Chen H., He H.
International Journal of Applied Mathematics and Computer Science
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2012
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tom 22
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nr 3
685-694
EN
Developing a model based digital human meridian system is one of the interesting ways of understanding and improving acupuncture treatment, safety analysis for acupuncture operation, doctor training, or treatment scheme evaluation. In accomplishing this task, how to construct a proper model to describe the behavior of human meridian systems is one of the very important issues. From experiments, it has been found that the hysteresis phenomenon occurs in the relations between stimulation input and the corresponding response of meridian systems. Therefore, the modeling of hysteresis in a human meridian system is an unavoidable task for the construction of model based digital human meridian systems. As hysteresis is a nonsmooth, nonlinear and dynamic system with a multi-valued mapping, the conventional identification method is difficult to be employed to model its behavior directly. In this paper, a neural network based identification method of hysteresis occurring in human meridian systems is presented. In this modeling scheme, an expanded input space is constructed to transform the multi-valued mapping of hysteresis into a one-to-one mapping. For this purpose, a modified hysteretic operator is proposed to handle the extremum-missing problem. Then, based on the constructed expanded input space with the modified hysteretic operator, the so-called Extreme Learning Machine (ELM) neural network is utilized to model hysteresis inherent in human meridian systems. As hysteresis in meridian system is a dynamic system, a dynamic ELM neural network is developed. In the proposed dynamic ELM neural network, the output state of each hidden neuron is fed back to its own input to describe the dynamic behavior of hysteresis. The training of the recurrent ELM neural network is based on the least-squares algorithm with QR decomposition.
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