Top PDF On Identification of Nonlinear Parameters in PDEs

On Identification of Nonlinear Parameters in PDEs

On Identification of Nonlinear Parameters in PDEs

Several methods exists to find this derivative, including the direct—or classical—method, as well as the popular adjoint method and adjoint stiffness method. The classical method is the most natural in that it finds Du(a)(δa) by solving the variational problem from Theorem 1.2.2 for the direction δa. This leads to the need of solving a forward problem once for each derivative direction δa which becomes quickly intractable for large problems. A much more efficient alternative is the adjoint method which requires only the solution of a single additional forward problem. Recent uses of the adjoint method can be found in [18, 19, 20, 21, 22, 23, 24, 25], and a survey article of first and second-order adjoint methods is given by Tortorelli and Michaleris [26]. The adjoint stiffness method builds on the adjoint equation but linearizes out the parameter a. The derivative can then be found directly with a single matrix equations, versus the adjoint method which has to evaluate Du(a)(δa) for each direction δa. The drawback of the adjoint stiffness method is that by its nature it is only applicable to linear parameters a. We are dealing with nonlinearly appearing parameters, and as such we can’t make use of the adjoint stiffness method. Instead we will utilize the adjoint method and develop expressions for the nonlinear parameter case.
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Identification of Linear and Nonlinear Parameters for Systems with Local Non Linearity

Identification of Linear and Nonlinear Parameters for Systems with Local Non Linearity

Nonlinear system identification is a fast evolving field of research with contributions from different communities, such as the mechanical engineering, systems and control, and civil engineering communities [1]. Many identification methods have been developed over the last years, for a wide variety of model structures. These methods can be classed into two sets. In the first set, the identification procedure is transformed into a state estimation problem after discretizing the differential equations into discrete state equations and treating the parameters as state variables. In the second set, identification of the nonlinear parameters from the measured data is formulated as an inverse problem and is often fulfilled by solving an optimization problem. Then, various techniques are proposed to deal with the state estimation problem or the optimization problem [2].
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Parabolic PDEs on evolving spaces

Parabolic PDEs on evolving spaces

on an evolving surface Γ(t), for m ≥ 1. We reformulate the equation as a local problem on the semi-infinite cylinder Γ(t) × [0, ∞), regularise the porous medium nonlinearity and truncate the cylinder. Then we pass to the limit first in the trun- cation parameter and then in the nonlinearity. The identification of limits is done using the theory of subdifferentials of convex functionals. In order to facilitate all of this, we begin by studying (in the setting of closed Riemannian manifolds and Sobolev spaces) the fractional Laplace–Beltrami operator which can be seen as the Dirichlet-to-Neumann map of a harmonic extension problem. A truncated harmonic extension problem will also be examined and convergence results of the solution to the (untruncated) harmonic extension will be given (these results are used in passing to the limit in the truncation described above). This theory is of course independent of the fractional porous medium equation and will be of use generally in the study of fractional elliptic and parabolic problems on manifolds. We will also consider some related extension problems on evolving hypersurfaces which will provide us with the language to formulate and solve the fractional porous medium equation (amongst others) on evolving hypersurfaces.
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Parameters Identification of Nonlinear DC Motor Model Using Compound Evolution Algorithms

Parameters Identification of Nonlinear DC Motor Model Using Compound Evolution Algorithms

DC motor has been widely used in the engineering field due to its simple structure, outstanding control performance and low cost. In high accuracy servo control system, high control performance of DC motor is needed. The traditional model of DC motor is a 2-order linear one, which ignores the dead nonlinear zone of the motor. Unfortunately, the dead zone caused by the nonlinear friction would bring great effect to servo systems. Therefore, it is vital to model the dead zone of DC motor accurately in order to improve the performance of servo system. Concerning the nonlinear friction of motor, Armstrong-helouvry B. et al. [1] have already conducted thorough research and proposed a nonlinear friction model. This friction model relates to the speed and time, and the motion goes four areas from the static friction to the coulomb friction. Moreover, this friction model structure is quite complex, and 7 parameters need to be identified. In order to simplify applications and reflect the real nonlinear friction of the motor accurately, a simplified friction model was proposed by Cong S. et al. [2], which is expressed as
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Complex Nonlinear System Modelling and Parameters Identification by Deep Neural Networks

Complex Nonlinear System Modelling and Parameters Identification by Deep Neural Networks

The real signals with multiple parameters were measured by interferometric fiber sensors. We obtained 7501 samples from each measurement. In the derived model, 𝜆 𝑚𝑖𝑛 = 1400 nm and ∆𝜆 = 0.04 nm. The first step for parameters identification is to determine parameter N. The signal was then transformed to frequency domain, where spectrum components that have high amplitude values were taken and calculated to estimate 𝑓 𝑖 . This results in the estimation of 𝑁 = 50.Then we built a 10 layers DNN model to estimate 𝐴 𝑖 , 𝑓 𝑖 and 𝜃 𝑖 . Some key hyper-parameters of DNN is listed below in table 1, where after 196 epochs of Pre-training, the loss value of second step was around 0.00001.
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Robust identification for linear in the parameters models

Robust identification for linear in the parameters models

Abstract: In this paper new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness, including three algorithms using combined A- or D-optimality or PRESS statistic (Predicted REsidual Sum of Squares) with regularised orthogonal least squares algorithm respectively. A common characteristic of these algorithms is that the inherent computation efficiency associated with the orthogonalisation scheme in orthogonal least squares or regularised orthogonal least squares has been extended such that the new algorithms are computationally efficient. A numerical example is included to demonstrate effectiveness of the algorithms. Copyright
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An Adaptive Nonlinear Filter for System Identification

An Adaptive Nonlinear Filter for System Identification

The primary difficulty in the identification of Hammerstein nonlinear systems (a static memoryless nonlinear system in series with a dynamic linear system) is that the output of the nonlinear system (input to the linear system) is unknown. By employing the theory of affine projection, we propose a gradient-based adaptive Hammerstein algorithm with variable step-size which estimates the Hammerstein nonlinear system parameters. The adaptive Hammerstein nonlinear system parameter estimation algorithm proposed is accomplished without linearizing the systems nonlinearity. To reduce the effects of eigenvalue spread as a result of the Hammerstein system nonlinearity, a new criterion that provides a measure of how close the Hammerstein filter is to optimum performance was used to update the step-size. Experimental results are presented to validate our proposed variable step-size adaptive Hammerstein algorithm given a real life system and a hypothetical case.
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Priming nonlinear searches for pathway identification

Priming nonlinear searches for pathway identification

Nonlinear estimation methods have been studied for a long time, and while computational and algorithmic effi- ciency will continue to increase, the combinatorial explo- sion of the number of parameters in systems with increasingly more variables mandates that identification tasks be made easier if larger systems are to be identified. One important possibility, which we pursue here, is to prime the iterative search with high-quality starting condi- tions that are better than naïve defaults. Clearly, if it is possible to identify parameter guesses that are relatively close to the true, yet unknown solution, the algorithm is less likely to get trapped in suboptimal local minima. We are proposing here to obtain such initial guesses by pre- processing the temporal profile data and fitting them pre- liminarily by straightforward multivariate linear regression. The underlying assumption is that the struc- tural and regulatory connectivity of the network will be reflected, at least qualitatively, in the regression coeffi- cients. D'haeseleer et al. [31] explored a similar approach for analyzing mRNA expression profiles, but could not validate their results because they lacked a mechanistic model of gene expression. Furthermore, because of the unique relationship between network structure and parameters in S-system models (see below), we will dem- onstrate that it is possible to translate the regression coef- ficients into constraints on the parameter values of an S- system model and thereby to reduce the parameter search space very dramatically.
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Application of the new approach of generalized (G' /G) -expansion method to find exact solutions of nonlinear PDEs in mathematical physics

Application of the new approach of generalized (G' /G) -expansion method to find exact solutions of nonlinear PDEs in mathematical physics

In this study, we considered the Zakharov-Kuznetsov-Benjamin-Bona-Mahony (ZK-BBM) equation. We employed the new approach of generalized ( G ′ / G ) -expansion method for the exact solution to this equation and constructed some new solutions which are not found in the previous literature. The method offers solutions with free parameters that might be imperative to explain some intricate physical phenomena. This study shows that the new generalized ( G ′ / G ) -expansion method is quite efficient and practically well suited to be used in finding exact solutions of NLEEs. Also, we observe that the new generalized ( G ′ / G ) -expansion method is straightforward and can be applied to many other nonlinear evolution equations.
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Identification of nonlinear systems using generalized kernel models

Identification of nonlinear systems using generalized kernel models

Choose the th regressor’s covariance matrix as . The important algorithmic parameters that need to be chosen appropriately are the maximum repeating times and the termination criterion . To further simplify control, we may simply let the loop repeat times. Then we only needs to set an appropriate value for . We have applied this repeated weighted optimization algorithm as a generic global optimizer in several difficult optimization applications [32], and analysis and empirical results given in [32] have shown that this guided random search algorithm is effective. The need to determine the diagonal covariance matrices of every candidate regressors represents additional computational complexity of the proposed generalized kernel modeling approach, in comparison with the standard kernel method. However, the standard kernel approach would typically require cross validation for specifying the common single kernel variance, and this may involve additional validation data set and can also be computationally expensive. The proposed method does not require cross validation to tune kernel parameters, which is an important practical advantage. B. LROLS Algorithm With LOO Test Score for Subset Model Selection
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A new class of wavelet networks for nonlinear system identification

A new class of wavelet networks for nonlinear system identification

Inspired by the well-known analysis of variance (ANOVA) expansions [28], [29], a new class of fixed grid WNs is intro- duced in the present study for nonlinear system identification. In the new WNs, the model structure of a high-dimensional system is initially expressed as a superimposition of a number of functions with fewer variables. By expanding each func- tion using truncated wavelet decompositions, the multivariate nonlinear networks can then be converted into linear-in-the-pa- rameter problems, which can be solved using least-squares type methods. The new WNs are, therefore, in structure different from either the existing WNs [18], [24]–[26], [30]–[32] or wavelet mutiresolution models [33], [34]. A wavelet multires- olution model is in structure similar to a fixed grid WN. The former, however, forms a wavelet multiresolution decompo- sition similar to an ordinary multiresolution analysis (MAR), which involves not only a wavelet, but also another function, the associated scaling function, where some additional require- ments should be satisfied. An efficient model term detection approach based on a forward orthogonal least squares (OLS) algorithm, along with the error reduction ratio (ERR) crite- rion [35]–[37] is applied to solve the linear-in-the-parameters problem in the present study.
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Identification of Nonlinear Systems Structured by Wiener-Hammerstein Model

Identification of Nonlinear Systems Structured by Wiener-Hammerstein Model

In [9] the identification method is based on the best linear approximation technique using class of Gaussian (-like) signals. In [10], the authors show that there are many local minima, the estimation must to be repeated several times with different starting values to increase the chances of finding a model corresponding to a good local minimum. In [11], an approach based on the standard SVM for regression was presented. The quite poor results obtained in that work highlighted some of the limitations of the method. In particular, only a NFIR model structure was taken into account, which did not perform well since the considered system has a long impulse response. Another problem was given by the high computational time and memory usage, which made it difficult to work with a large amount of data. Several SVM-like approaches (e.g. [12]-[13]), based on the least squares SVM (LSSVM), are characterized by a very high number of parameters.
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Some stochastic particle methods for nonlinear 
parabolic PDEs

Some stochastic particle methods for nonlinear parabolic PDEs

For each method, accuracy (measured by the difference with an exact solution), computational time and storage are quantities defining the global efficiency of the method. Here, only the computational time (CPU time) is considered. In the following numerical tests, the simulation parameters have been tuned in order to require a computational time of one, five or ten minutes, on the same computer, with similarly optimized FORTRAN programs.

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On the orthogonalised reverse path method for nonlinear system identification

On the orthogonalised reverse path method for nonlinear system identification

conditioned spectral analysis which yield the underlying linear FRF can corrupt or bias the estimates of the nonlinear term parameters. The first objective of the current paper has been to suggest some simple strategies for improving the parameter estimates for the nonlinear terms. The basic ideas are (a) to bound the frequencies over which the parameter spectra are averaged to exclude high frequency fluctuations, (b) to improve the coefficient estimates by using weighting averages and (c) to estimate the parameters in isolation from each other in order to avoid accumulation of errors due to recursion. The overall novelty of this work is possibly in question as previous studies may simply have implicitly adopted these strategies; however, the current authors feel there is some benefit in presenting a summary. Numerical simulations have been carried out in order to study the influence of these strategies on the parameter estimates and some benefit has been demonstrated. The actual origin of the artefacts in the coefficient spectra as a result of the conditioning process and estimation of the linear FRF matrix has not been discussed in any detail and further work is in progress on this matter.
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Limit Cycle Identification in Nonlinear Polynomial Systems

Limit Cycle Identification in Nonlinear Polynomial Systems

The concepts related to limit cycles and polynomials are introduced in Section 2. Then we present in Section 3 a two-level approach based on Macaulay matrix to solve for the limit cycle. In Section 4, we review the concept of immersion and its novel use to extend our framework to non-polynomial limit cycle identification. Section 5 de- monstrates the entire procedure with examples. Finally, Section 6 concludes this paper.

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STATIC ANALYSIS OF COMPOSITE LEAF SPRING WITH NONLINEAR PARAMETERS

STATIC ANALYSIS OF COMPOSITE LEAF SPRING WITH NONLINEAR PARAMETERS

In this paper Numerical analysis of composite leaf spring is carried out to find out effect of nonlinearities. For Linear and Nonlinear Finite element analysis ANSYS software is used. Experimental set up is developed in order to find out load-displacement characteristics of Composite Leaf spring. Load- displacement characteristics are studied in order to find out nonlinearities present in composite leaf spring. The present work deals with the numerical analysis of composite leaf spring with nonlinear parameters. In this paper Material and Geometric nonlinearities are considered.
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Neuro PID control for nonlinear plants 
		with variable parameters

Neuro PID control for nonlinear plants with variable parameters

So, for example, in [2], to realize a nonlinear PID-regulator, the 2-layer artificial neural network of direct distribution containing 8 neurons of the hidden layer with sigmoid activation functions and one neuron of the output layer with linear activation function (Figure-1) is used.

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Kadomtsev-Petviashvili (Kp) nonlinear waves identification

Kadomtsev-Petviashvili (Kp) nonlinear waves identification

For future research, we are looking into telecommunication sector, the nano– technology and also optical solitons. In 1973 Hasegawa and Tappert had proposed that soliton pulses could be used in optical communications through the balance of non- linearity and dispersion. They showed that these solitons would propagate according to the nonlinear Schrodinger equation (NLS), which had been solved by the inverse scattering method a year earlier by Zakharov and Shabat. At that time there was no capability to produce the fibers with the proper characteristics for doing this and the dispersive properties of optical fibers were not known. Also, the system required a laser which could produce very small wavelengths, which also was unavailable. It wasn’t until seven years later, when Mollenauer, Stollen and Gordon at AT&T Bell Laboratories had experimentally demonstrated the propagation of solitons in optical fibers. The original communications systems employed pulse trains with widths of about one nanosecond. However, there was still some distortion due to fiber loss. This was corrected by placing repeaters every several of tens of kilometers. As the width of the available pulses was decreased, the spacing of the repeaters was increased. In the mid 1980’s it was proposed that by sending in an additional pump wave along the fiber, the dispersion of a soliton could be halted through a process known as Raman scattering. In 1988 Mollenauer and his group had shown that this could be done by propagating a soliton over 6000 km without the need for repeaters.
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Stochastic PDEs with multiscale structure

Stochastic PDEs with multiscale structure

We study the spatial homogenisation of parabolic linear stochastic PDEs exhibiting a two-scale structure both at the level of the linear operator and at the level of the Gaussian driving noise. We show that in some cases, in particular when the forcing is given by space-time white noise, it may happen that the homogenised SPDE is not what one would expect from existing results for PDEs with more regular forcing terms.

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Parameters identification and strategies for modal updating

Parameters identification and strategies for modal updating

The 3 rd International Conference on Numerical Analysis in Engineering, Pula u Batam, Indonesia ..[r]

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