2 edition of **Convergence properties of the parameter vector in real-time identification of linear systems** found in the catalog.

Convergence properties of the parameter vector in real-time identification of linear systems

Shmuel Merhav

- 71 Want to read
- 33 Currently reading

Published
**1972**
by Technion, Israel Institute of Technology, Dept. of Aeronautical Engineering in Haifa, Israel
.

Written in English

- System identification.,
- Linear systems.,
- Convergence.,
- Parameter estimation.,
- Vector analysis.

**Edition Notes**

Statement | by S.J. Merhav and E. Gabay. |

Series | T.A.E. report -- no. 166 |

Contributions | Gabay, E., Ṭekhniyon, Makhon ṭekhnologi le-Yiśraʾel. Maḥlaḳah le-handasah aṿironotit |

The Physical Object | |
---|---|

Pagination | vii, 48 leaves : |

Number of Pages | 48 |

ID Numbers | |

Open Library | OL19645819M |

In solving robust linear regression problems, the parameter vector x, as well as an additional parameter s that scales the residuals, must be estimated simultaneously. A widely used method for doing so consists of first improving the scale parameter s for fixed x, and then improving x for fixed s by using a quadratic approximation to the objective function by: Modelling and Systems Parameter Estimation for Dynamic Systems presents a detailed examination of the estimation techniques and modeling problems. The theory is furnished with several illustrations and computer programs to promote better understanding of system modeling and parameter estimation. The material is presented in a way that makes for easy reading and enables the user to implement Cited by:

A Nonlinear Conjugate Gradient Method with a Strong Global Convergence Property. Related Databases. Web of Science () Parameter identification problems and analysis of the impact of porous media in biofluid heat transfer in biological tissues during thermal by: The concepts of detectability and stabilizability are explored for time-varying systems. We study duality, invariance under feedback, an extended version of the lemma of Lyapunov, existence of stabilizing feedback laws, linear quadratic filtering and control, and the existence of approximate canonical by:

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For the assumption of bounded measurement errors, a parameter vector p is considered infeasible, if the absolute deviation between any model output y i (t j; p) from the respective unperturbed measurement y ˜ i t j at any time t j is greater than defined bounds ε i, j.

Hence, the respective constraint formulation is. Parameter identification of linear time‐invariant systems using dynamic regressor extension and mixing vector that is a much stronger property than monotonicity of the vector norm, as. By introducing an auxiliary vector uncorrelated with the noise, the consistent parameter estimation is obtained without the strictly positive real (SPR) condition.

Convergence analysis of the recursive algorithm is performed using the ordinary differential equation (ODE) method. The simulation results validate the algorithm by: 8.

The convergence property of the proposed algorithm is intensively studied and the conditions for the uniform convergence of parameter estimations are also obtained. Furthermore, the problems related to the proposed algorithm are discussed and it is concluded that the parameter estimate is still bounded provided that there is any mismatch on system model or process noise acting on the by: Tables 10and 11 demonstrate typical results on the convergence of the algorithm with and without noise, respectively.

In Ta we show the mean and standard deviation of the parameters between convergent runs of the algorithm when the noise level is Fig. 4, we constructed a box plot based on simulations for several noise levels We summarized the results of runs when the Cited by: 1.

This paper studies the identification of linear systems with quantized output observations. for global parametric convergence to the true parameter vector w.p. An online approach to. USA ROBUST REAL-TIME IDENTIFICATION OF LINEAR MULTIVARIABLE SYSTEMS B. Kovacevic and S. Stankovic Faculty of Electrical Engineering, University of Belgrade, Belgrade, Yugoslavia Abstract.

The paper presents a discussion on robust real-time estimation of parameters in linear multiple input-multiple output discrete-time dynamic by: 3. A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text.

follow slowly varying parameters and they can ensure at most exponential or asymptotic convergence to a neighborhood of the real the context of ﬁnite-time (FT) convergence [10], a recursive FT convergent algorithm has been presented in [11].

Such an algorithm is a non-linear recursive versionCited by: The land surface model used in this study to simulate soil moisture is the JULES model—a widely used tiled model of subgrid heterogeneity that simulates water and energy fluxes between a vertical profile of soil layers, vegetation, and the atmosphere (Best et al.

).The JULES model uses meteorological forcing data, surface land cover data, soil properties data, and values for Cited by: 8. Unlike previous uses of the stochastic stability lemma for stability proof, this new convergence analysis technique considers time-varying parameters, which can be calculated online in real-time to monitor the performance of the by: An abstract approximation theory for the identification of linear degenerate distributed parameter systems is developed.

Central to the approach is an abstract approximation result for regular and degenerate implicit distributed systems in the spirit of the Trotter-Kato Theorem for the approximation of linear semigroups. As the vector field in is separable in the linear parameter vector, we refer to the corresponding ODE system as linear in the parameter (cf.

the case of a linear regression model), although the solution to the system might be highly nonlinear in θ, or even implicit. Example 2. Let Then, one sees that equation is a special case of –. Identification of linear systems from noisy data.

[] Proceedings of the 30th IEEE Conference on Decision and Control, Recursive least squares algorithm with compensation for coloured by: In DAIC provided where and is output of estimated plant given by where is a parameter vector for defined as and is regression vector defined as.

Development of Estimation Algorithm for SISO Systems. In this section, estimation algorithms for linear SISO systems are developed. Parameter estimation is developed based on NLMS algorithm. Cited by: 2. Identification and System Parameter Estimation, R. Kashyap, “Estimation of parameters in a partially whitened representation of a stochastic process,” IEEE Trans.

Automat. Convergence evaluation of a random step-size NLMS adaptive algorithm in system identification. the convergence properties of a variable step normalized LMS (VSNLMS) adaptive filter algorithm. parameters from our prior parameter distribution, and use these in the model to generate new data samples.

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Verifying Numerical Convergence Rates 1 Order of accuracy We consider a numerical approximation of an exact value u. The approximation depends on a small parameter h, such as the grid size or time step, and we denote it by u˜h.

If the numerical method is of order p, we mean that there is a number C independent of h such that |u˜h −u File Size: KB.The field's leading text, now completely updated.

Modeling dynamical systems — theory, methodology, and applications. Lennart Ljung's System Identification: Theory for the User is a complete, coherent description of the theory, methodology, and practice of System Identification.

This completely revised Second Edition introduces subspace methods, methods that utilize frequency domain data 4/5(2).Despite the complications arising from this possibility, the method achieves a linear convergence rate on functions that satisfy an optimal strong convexity property and Cited by: