Posted by 
By Eric Walter, Luc Pronzato, J. Norton
The presentation of a coherent technique for the estimation of the parameters of mathematical versions from experimental facts is tested during this quantity. Many subject matters are lined together with the alternative of the constitution of the mathematical version, the alternative of a functionality criterion to match versions, the optimization of this functionality criterion, the evaluate of the uncertainty within the predicted parameters, the layout of experiments as a way to get the main proper facts and the serious research of effects. There also are numerous positive factors precise to the paintings reminiscent of an updated presentation of the technique for checking out types for identifiability and distinguishability and a accomplished remedy of parametric optimization including higher examine ation of numerical elements and which examines recursive and non-recursive tools for linear and nonlinear types.
Read Online or Download Identification of parametric models from experimental data PDF
Similar algorithms and data structures books
Vorlesungen über Informatik: Band 1: Grundlagen und funktionales Programmieren
Goos G. , Zimmermann W. Vorlesungen ueber Informatik, Band 1. . Grundlagen un funktionales Programmieren (ISBN 3540244050)(de)(Springer, 2005)
Algorithms and Protocols for Wireless Sensor Networks
A one-stop source for using algorithms and protocols in instant sensor networks From a longtime foreign researcher within the box, this edited quantity offers readers with finished insurance of the basic algorithms and protocols for instant sensor networks. It identifies the learn that should be carried out on a couple of degrees to layout and investigate the deployment of instant sensor networks, and offers an in-depth research of the advance of the following new release of heterogeneous instant sensor networks.
Algorithmic Foundations of Geographic Information Systems
This instructional survey brings jointly traces of analysis and improvement whose interplay grants to have major useful impression at the sector of spatial details processing within the close to destiny: geographic info structures (GIS) and geometric computation or, extra rather, geometric algorithms and spatial info buildings.
There are numerous facts communications titles masking layout, deploy, and so forth, yet virtually none that particularly specialise in commercial networks, that are an important a part of the day by day paintings of commercial keep watch over platforms engineers, and the focus of an more and more huge workforce of community experts.
Extra info for Identification of parametric models from experimental data
Sample text
Due to the various perturbations llcting on the system, one will usually not obtain exactly the same results when the same experiment is repeated; Ys is therefore a random vector. e. any p that corresponds to an optimal value of the cost function J, an estimate of p in the sense of j. 1 Least squares Quadratic cost functions are by far the most commonly used, since Gauss and Legendre (Stigler, ] 981), because of their intuitive appeal and relatively easy optimization (for LP models, the best estimate in the sense of a quadratic cost function can be obtained analytically, as will be seen in Chapler 4).
For example, to work with a relative error, it suffices to set Wik = 1IIYk(tik) I. 7. Note, however, that the estimate obtained may not be unique, even when a least-squares estimate would be. For instance, j(P) = Ipl + Ip - 31 is at its minimum over [0, 3]. 3. For a detailed presentation of the statistical properties of Lt estimators and of techniques LJU crtU'r/ll to compute them, one may consult (Bloomfield and Steiger. 1983; Dodge, 1987; Gonin and Money, 1989). 4. 3 Maximum likelihood The vector Pml will be a maximum-likelihood estimate if it maximizes the cm\l function If p were fixed, JI'y(ySlp) would be the probability density of the random vector yS being generated by a model with parameters p.
The maximum-likelihood method looks for the value of the parameter vector p that gives the highest likelihood to the observed data. This approach allows one to take into account in the design of the cost the available information on the nature of the noise acting on the system. In practice, it is often easier to look for by maximizing the log-likelihood function Pml which yields the same estimate since the logarithm function is monotonically increasing. ) according to a Gaussian law with mean Pi and variance We wish to estimate Pi and in the maximum-likelihood sense.