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1

A Novel Kernel Algorithm for Finite Impulse Response Channel Identiﬁcation

Fateh Rachid, Darif Anouar, Boumezzough Ahmed, Saﬁ Said, Frikel Miloud

Journal of Telecommunications and Information Technology

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2023

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nr 2

84--93

EN

Over the last few years, kernel adaptive ﬁlters have gained in importance as the kernel trick started to be used in classic linear adaptive ﬁlters in order to address various regression and time-series prediction issues in nonlinear environments.In this paper, we study a recursive method for identifying ﬁnite impulse response (FIR) nonlinear systems based on binary-value observation systems. We also apply the kernel trick to the recursive projection (RP) algorithm, yielding a novel recursive algorithm based on a positive deﬁnite kernel. For purposes, our approach is compared with the recursive projection (RP) algorithm in the process of identifying the parameters of two channels, with the ﬁrst of them being a frequency-selective fading channel, called a broadband radio access network (BRAN B) channel, and the other being a a theoretical frequency-selective channel, known as the Macchi channel. Monte Carlo simulation results are presented to show the performance of the proposed algorithm.

2

Nonlinear system identification of a MIMO quadruple tanks system using NARX model

Araújo Ícaro B. Q., Cavalcante Gabriel B., Lúcio Yan L. S., Araújo Fábio M. U.

Przegląd Elektrotechniczny

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2019

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R. 95, nr 6

66--72

EN

This paper has two main objectives. First, it gives an overview on the identification of MIMO nonlinear systems using NARX models. It covers the classical approach of the FROLS method, as well as the SEMP method. The second is to present some new useful results in model structure selection for NARX polynomial models applied to MIMO systems. It shows how to make a representation of MIMO systems from NARX polynomial models and the application of classical methods to identify these models. The study case used is a real didactic quadruple tank system manufactured by Quanser.

PL

Artykuł ma dwa cele. Po pierwsze przedstawia przegląd metod identyfikacji nieliniowych systemów MIMO przy użyciu modelu NARX. Przedstawiono klasyczną metodę FROLS a także metodę SEMP. Po drugie przedstawiono użyteczne wyniki selekcji struktury wielomianowego modelu NARX zastosowanego do systemów MIMO.

3

Nonlinear system identification using heterogeneous multiple models

Orjuela R., Marx B., Ragot J., Maquin D.

International Journal of Applied Mathematics and Computer Science

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2013

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Vol. 23, no. 1

103--115

EN

Multiple models are recognised by their abilities to accurately describe nonlinear dynamic behaviours of a wide variety of nonlinear systems with a tractable model in control engineering problems. Multiple models are built by the interpolation of a set of submodels according to a particular aggregation mechanism, with the heterogeneous multiple model being of particular interest. This multiple model is characterized by the use of heterogeneous submodels in the sense that their state spaces are not the same and consequently they can be of various dimensions. Thanks to this feature, the complexity of the submodels can be well adapted to that of the nonlinear system introducing flexibility and generality in the modelling stage. This paper deals with off-line identification of nonlinear systems based on heterogeneous multiple models. Three optimisation criteria (global, local and combined) are investigated to obtain the submodel parameters according to the expected modelling performances. Particular attention is paid to the potential problems encountered in the identification procedure with a special focus on an undesirable phenomenon called the no output tracking effect. The origin of this difficulty is explained and an effective solution is suggested to overcome this problem in the identification task. The abilities of the model are finally illustrated via relevant identification examples showing the effectiveness of the proposed methods.

4

An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection

Mrugalski M.

International Journal of Applied Mathematics and Computer Science

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2013

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Vol. 23, no. 1

157--169

EN

This paper presents an identification method of dynamic systems based on a group method of data handling approach. In particular, a new structure of the dynamic multi-input multi-output neuron in a state-space representation is proposed. Moreover, a new training algorithm of the neural network based on the unscented Kalman filter is presented. The final part of the work contains an illustrative example regarding the application of the proposed approach to robust fault detection of a tunnel furnace.

5

Sensitivity analysis of an identification method dedicated to nonlinear systems working under operational loads

Iwaniec J.

Journal of Theoretical and Applied Mechanics

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2011

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Vol. 49 nr 2

419-438

EN

In the paper, the exploitational nonlinear systems identification method based on algorithms of the restoring force, boundary perturbations and direct parameter identification methods is presented. The obtained parameter estimates provide information concerning forces transferred on the foundation and find application in the model-based diagnostics. The results of the sensitivity analysis carried out in order to assess the influence of input parameters uncertainties (accuracy of resonant frequency and amplitude estimates, errors of transfer function estimation in operational conditions, value of introduced additional mass) on the accuracy of estimated system parameters are also presented.

PL

W pracy przedstawiono metodę operacyjnej identyfikacji parametrów modeli nieliniowych konstrukcji mechanicznych, realizowaną w oparciu o algorytmy metody sił resztkowych, zaburzeń brzegowych oraz bezpośredniej identyfikacji parametrów. Uzyskane estymaty parametrów dostarczają informacji o siłach przekazywanych na pod- łoże i znajdują zastosowanie w diagnostyce realizowanej w oparciu o model układu nieuszkodzonego. W celu oszacowania wpływu niepewności parametrów wejściowych na dokładność estymowanych parametrów układu, przeprowadzono analizę wrażliwości poszukiwanych parametrów układu (masy, sztywności, tłumienia) na dokładność estymacji częstotliwości i amplitud rezonansowych (uwarunkowaną błędami estymacji funkcji przejścia w warunkach eksploatacyjnych), a także wartość masy wprowadzanej do układu w celu zmodyfikowania jego własności dynamicznych (zgodnie z algorytmem metody zaburzeń brzegowych).

6

Heuristic modeling using recurrent neural networks: simulated and real-data experiments

Przystałka P.

Computer Assisted Mechanics and Engineering Sciences

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2007

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Vol. 14, No. 4

715-727

EN

The focus of this paper is on the problems of system identification, process modeling and time series forecasting which can be met during the use of locally recurrent neural networks in heuristic modeling technique. However, the main interest of this paper is to survey the properties of the dynamic neural processor which is developed by the author. Moreover, a comparative study of selected recurrent neural architectures in modeling tasks is given. The results of experiments showed that some processes tend to be chaotic and in some cases it is reasonable to use soft computing models for fault diagnosis and control.