Nonlinear black-box models in system identification: Mathematical foundations

Juditsky, Anatoli; Hjalmarsson, Håkan; Benveniste, Albert; Delyon, Bernard; Ljung, Lennart; Sjöberg, Jonas; Zhang, Qinghua

doi:10.1016/0005-1098(95)00119-1

Cited by 344 publications

(155 citation statements)

References 29 publications

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“…Although there are many methods for identifying nonlinear stochastic systems in a TF form (see e.g., the review in Juditsky et al [1995]), the main approach used so far in DBM modeling is nonparametric, SDP estimation, as implemented by the sdp routine in the CAPTAIN Toolbox. This helps to identify the location and graphical nature of significant nonlinearities in the model preparatory to the parameterization of these nonlinearities at the next estimation stage.…”

Section: Model Structure Identificationmentioning

confidence: 99%

Hypothetico‐inductive data‐based mechanistic modeling of hydrological systems

Young

2013

Water Resources Research

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[1] The paper introduces a logical extension to data-based mechanistic (DBM) modeling, which provides hypothetico-inductive (HI-DBM) bridge between conceptual models, derived in a hypothetico-deductive manner, and the DBM model identified inductively from the same time-series data. The approach is illustrated by a quite detailed example of HI-DBM analysis applied to the well-known Leaf River data set and the associated HyMOD conceptual model. The HI-DBM model significantly improves the explanation of the Leaf River data and enhances the performance of the original DBM model. However, on the basis of various diagnostic tests, including recursive time-variable and state-dependent parameter estimation, it is suggested that the model should be capable of further improvement, particularly as regards the conceptual effective rainfall mechanism, which is based on the probability distributed model hypothesis. In order to verify the efficacy of the HI-DBM analysis in a situation where the actual model generating the data is completely known, the analysis is also applied to a stochastic simulation model based on a modified HyMOD model.

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Section: Model Structure Identificationmentioning

confidence: 99%

Hypothetico‐inductive data‐based mechanistic modeling of hydrological systems

Young

2013

Water Resources Research

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“…System identification is only concerned the system that has signal input and output from the real experimental data. Furthermore, it may takes less time than from physical modeling [1]. System identification methods aim to find appropriate models for the real physical systems.…”

Section: Introductionmentioning

confidence: 99%

Application of Firefly Algorithm for Parameter Estimation of Damped Compound Pendulum

et al. 2016

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Abstract. This paper presents an investigation into the parameter estimation of the damped compound pendulum using Firefly algorithm method. In estimating the damped compound pendulum, the system necessarily needs a good model. Therefore, the aim of the work described in this paper is to obtain a dynamic model of the damped compound pendulum. By considering a discrete time form for the system, an autoregressive with exogenous input (ARX) model structures was selected. In order to collect input-output data from the experiment, the PRBS signal is used to be input signal to regulate the motor speed. Where, the output signal is taken from position sensor. Firefly algorithm (FA) algorithm is used to estimate the model parameters based on model 2nd orders. The model validation was done by comparing the measured output against the predicted output in terms of the closeness of both outputs via mean square error (MSE) value. The performance of FA is measured in terms of mean square error (MSE).

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“…The estimation of regression function from a data set is usually performed under the assumption that the error terms are iid (Juditsky et al, 1995). This assumption is not satisfied when the correlation is present in the given data (e.g.…”

Section: Introductionmentioning

confidence: 99%

Support vector quantile regression for autoregressive data

Hwang¹

2014

Journal of the Korean Data and Information Science Society

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In this paper we apply the autoregressive process to the nonlinear quantile regression in order to infer nonlinear quantile regression models for the autocorrelated data. We propose a kernel method for the autoregressive data which estimates the nonlinear quantile regression function by kernel machines. Artificial and real examples are provided to indicate the usefulness of the proposed method for the estimation of quantile regression function in the presence of autocorrelation between data.

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Nonlinear black-box models in system identification: Mathematical foundations

Cited by 344 publications

References 29 publications

Hypothetico‐inductive data‐based mechanistic modeling of hydrological systems

Hypothetico‐inductive data‐based mechanistic modeling of hydrological systems

Application of Firefly Algorithm for Parameter Estimation of Damped Compound Pendulum

Support vector quantile regression for autoregressive data

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