direct approach to identification of nonlinear differential models from discrete data

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  • English
University of Sheffield, Dept. of Automatic Control and Systems Engineering , Sheffield
StatementD. Coca, S.A. Billings.
SeriesResearch report / University of Sheffield. Department of Automatic Control and Systems Engineering -- no.709, Research report (University of Sheffield. Department of Automatic Control and Systems Engineering) -- no.709.
ContributionsBillings, S. A.
ID Numbers
Open LibraryOL17519568M

A Direct Approach to Identification of Nonlinear Differential Models from Discrete Data D. Coca S.A. Billings and Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield 3JD, UK Abstract The paper introduces a direct approach to the identification of nonlinear differential equations from noisy input/output data.

The paper introduces a direct approach to the identification of non-linear differential equations from noisy input/output data.

Both the parameter estimation and the structure determination problems are by:   The paper introduces a direct approach to the identification of non-linear differential equations from noisy input/output data. Both the parameter estimation and the structure determination problems are addressed. Central to the proposed methodology are two algorithms, a numerical differentiation algorithm involving fixed interval Kalman smoothing and an orthogonal regression Cited by: A Direct Approach to Identification of Nonlinear.

An advantage of modelling linear systems in the δ-domain is that, whilst it provides an exact discrete-time representation of the system, the identified model has structural similarity to the continuous-time differential equation describing system dynamics; additionally the parameters of the identified model approach the continuous-time values.

approaches involve the identification of discrete time models as a first step and then the transfer of the discrete-time models to continuous time models (Li & Billings, ). The transfer is based on some invariant properties between the discrete and continuous models, for example, the impulse response, the frequency response functions and so on.

The stability of T-S model based fuzzy control systems for discrete, linear time-invariant plant and nonlinear time varying plant is analyzed with the help of virtual equivalent system concept and.

By using ISS approach, this paper gives conclusions that the commonly used robustifying techniques for system identification, such as dead-zone and σ-modification, are not necessary for the gradient descent law and the backpropagation-like algorithm when discrete-time recurrent neural networks are used.

models direct from the discrete data, involving both model S.A. A direct approach to identifi-cation of nonlinear differential models from discrete data. A Nonlinear Filtering Approach to Changepoint Detection Problems: Direct and Differential-Geometric Methods.

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Web of Science which is designed using differential-geometric methods in a suitably chosen space of unnormalized probability densities. The new nonlinear filter can be interpreted as an adaptive version of the. The approach offers a number of potential advantages, but questions remain over its applicability to lowland UK catchments, its use with 15 min data, the most appropriate model identification and.

Nonlinear model identification requires uniformly sampled time-domain data. Your data can have one or more input and output channels. You can also model time series data using nonlinear ARX and nonlinear grey-box models.

For more information, see About Identified Nonlinear Models. According to this method, the equivalence between nonlinear differential and difference equation models is defined on the basis of having the discrete high-order kernels (corresponding to nonlinear difference equations) be the sampled versions of their continuous counterparts (corres- ponding to the equivalent nonlinear differential equation).

A dimension reduction method called discrete empirical interpolation is proposed and shown to dramatically reduce the computational complexity of the popular proper orthogonal decomposition (POD) method for constructing reduced-order models for time dependent and/or parametrized nonlinear partial differential equations (PDEs).

Initially, the paper provides an introduction to the main aspects of existing time-domain methods for identifying linear continuous-time models from discrete-time data and shows how one of these methods has been applied to the identification and estimation of a model for the transportation and dispersion of a pollutant in a river.

This paper proposes a new approach to control nonlinear discrete dynamic systems, which relies on the identification of a discrete model of the system by a neural network. UNESCO – EOLSS SAMPLE CHAPTERS CONTROL SYSTEMS, ROBOTICS, AND AUTOMATION - Vol. VI - Identification of Nonlinear Systems - H.

Unbehauen ©Encyclopedia of Life Support Systems (EOLSS) • a differential equation representing a continuous-time model, • a difference equation representing a discrete-time model, • a continuous or discrete state-space representation. System identification is a method of identifying or measuring the mathematical model of a system from measurements of the system inputs and outputs.

The applications of system identification include any system where the inputs and outputs can be measured and include industrial processes, control systems, economic data, biology and the life sciences, medicine, social systems and many more.

This method uses symbolic regression [i.e., genetic programming ] to find nonlinear differential equations, and it balances complexity of the model, measured in the number of terms, with model accuracy.

The resulting model identification realizes a long-sought goal of the physics and engineering communities to discover dynamical systems from data. And the additive noise model (ANM), which includes three types: linear, non-linear and discrete, is one of the widely used functional models [20]- [22].

It had been shown that many real-world. Here, T s = seconds and NT s is the time of the last measurement. If you want to build a discrete-time model from this data, the data vectors u meas and y meas and the sample time T s provide sufficient information for creating such a model.

If you want to build a continuous-time model, you must also know the intersample behavior of the input signals during the experiment.

Nonlinear autoregressive moving average with the exogenous input (NARMAX) model [89,98,99] is a general description of discrete-time nonlinear nonaffine systems.

Details direct approach to identification of nonlinear differential models from discrete data EPUB

However, the controller design for such a model is quite difficult due to the intrinsic nonlinearity with respect to control input. As t approaches the critical value t⋆ = t 0+1/u 0 from below, the solution “blows up”, meaning u(t) → ∞ as t → t⋆.

The blow-up time t⋆ depends upon the initial data — the larger u 0 > 0 is, the sooner the solution goes off to infinity. If the initial data is negative, u 0 t. Algebraic Identification and Estimation Methods in Feedback Control Systems presents a model-based algebraic approach to online parameter and state estimation in uncertain dynamic feedback control systems.

This approach evades the mathematical intricacies of the traditional stochastic approach, proposing a direct model-based scheme with several easy-to-implement computational.

The paper offers an approach to the investigation of the dynamics of nonlinear non-stationary processes with the focus on the risk of dynamic system stability loss.

The risk is assessed on the basis of the accumulated knowledge about power supply system operation. New methods for power supply modes analysis are developed and applied as follows: linear discrete point knowledge-based models.

Data Identification Model Experiment Plant Rarely used in real-life control. Impulse response identification • Simplest approach: apply control impulse and collect the data model Nonlinear Regression ID Nonlinear Regression ID.

EEm - Winter Control Engineering Identification of Nonlinear Vibrating Structures: Part I-Formulation A self-starting multistage, time-domain procedure is presented for the identification of nonlinear, multi-degree-of-freedom systems undergoing free oscillations or sub- jected to arbitrary direct force.

Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains describes a comprehensive framework for the identification and analysis of nonlinear dynamic systems in the time, frequency, and spatio-temporal domains.

This book is written with an emphasis on making the algorithms accessible so that they can be applied and used in practice. An instrumental variable method for robot identification based on time variable parameter estimation P.R.

Bélanger P. Dobrovolny A.

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Helmy X. Zhang Estimation of angular velocity and acceleration from shaft-encoder measurements. Instead of deriving an approximate coarse-grained continuum model and discretizing it, we suggest directly learning low-resolution discrete models that encapsulate unresolved physics.

Rigorous mathematical work shows that the dimension of a solution manifold for a nonlinear PDE is finite (25, 26) and that approximate parameterizations can be. () Smooth functional tempering for nonlinear differential equation models. Statistics and Computing() A Simultaneous Approach for Parameter Estimation of a System of Ordinary Differential Equations, Using Artificial Neural Network Approximation.I do not mean to say that these techniques or approaches are useless.

Certainly phase-plane analysis describes nonlinear phenomena such as limit cycles and multiple equilibria of second-order systems in an efficient manner.

The theory of differential equations has led to a highly developed stability theory for some classes of nonlinear systems.Econometrica, Vol. 74, No. 2 (March, ), – IDENTIFICATION AND INFERENCE IN NONLINEAR DIFFERENCE-IN-DIFFERENCES MODELS BY SUSAN ATHEY AND GUIDO W. I MBENS1 This paper develops a generalization of the widely used difference-in-differences.