A New Approach To Parameter Estimation For Large Scale Processes

Author : Lorenz T. Biegler
Publisher :
ISBN 13 :
Total Pages : 24 pages
Book Rating : 4.:/5 (245 download)

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Download or read book A Parallel Implementation for Parameter Estimation with Implicit Models written by Lorenz T. Biegler and published by . This book was released on 1992 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Parameter estimation and data regression represent special classes of optimization problems. Often, nonlinear programming methods can be tailored to take advantage of the least squares, or more generally, the maximum likelihood, objective function. In previous studies we have developed large-scale nonlinear programming methods that are based on tailored quasi-Newton updating strategies and matrix decomposition of the process model. The resulting algorithms converge in a reduced space of the parameters while simultaneously converging the process model. Moreover, tailoring the method to least squares functions led to significant improvements in the performance of the algorithm. These approaches can be very efficient for both explicit and implit [sic] models (i.e., problems with small and large degrees of freedom, respectively). In the latter case, degrees of freedom are proportional to a potentially large number of data sets. Applications of this case include errors-in- all-variables estimation, data reconciliation and identification of process parameters. To deal with this structure, we apply a decomposition approach that performs a quadratic programming factorization for each data set. Because these are small components of large problems, an efficient and reliable algorithm results. These methods have generally been implemented on standard von Neumann architectures and few studies exist that exploit the parallelism of nonlinear programming algorithms. It is therefore interesting to note that for implicit model parameter estimation and related process applications, this approach can be quite amenable to parallel computation, because the major cost occurs in matrix decompositions for each data set. Here we describe an implementation of this approach on the Alliant FX/8 parallel computer at the Advanced Computing Research Facility at Argonne National Laboratory. Special attention is paid to the architecture of this machine and its effect on the performance of the algorithm. This approach is demonstrated on five small, undetermined regression problems as well as a larger process example that demonstrates simultaneous data reconciliation and parameter estimation."