Application of Evolutionary Algorithms in Guaranteed Parameter Estimation
Loading...
Date
2016-11
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Model-based optimization and control is
becoming more and more important in the process industries and
in general. Modelling almost always involves the estimation of
parameters from available data. The parameter estimation
problem is usually posed as the minimization of the prediction
error or the maximization of the likelihood function. If the
uncertainty of the measurements taken from a real process is
assumed to be an interval around the measured values, a set of
parameter vectors exists that is able to describe the behavior of
the systems within these uncertainties. Guaranteed parameter
estimation deals with the problem of determining all parameter
vectors that are compatible with uncertain observations. The
solution of guaranteed parameter estimation problems for
nonlinear dynamic models is computationally very demanding.
In this contribution we present a memetic algorithm that
determines the sets of feasible model parameters efficiently. It is
applied to the estimation of kinetic parameters of a model that
describes a copolymerization reaction. In the memetic algorithm,
the fitness evaluation is based on the distance of the feasible
solutions to each other, thus the presented approach is not
restricted to a specific type of models.
Description
Table of contents
Keywords
guaranteed parameter estimation, memetic algorithm, measurement uncertainty