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The αBB Approach in Parameter Estimation

  • Christodoulos A. Floudas
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 37)

Abstract

In this chapter, we present a global optimization framework for the parameter estimation of nonlinear algrebraic models through the error-in-variables approach. Section 19.1 provides the motivation and reviews the previous research contributions. Section 19.2 introduces the basics of the maximum likelihood parameter estimation. Section 19.3 presents the global optimization approach which is based on the general principles of the αBB with a number of modifications. Section 19.4 describes the detailed algorithmic steps of the global optimization approach as it is applied to the parameter estimation problem. Section 19.5 presents representative computational studies and comparisons with interval analysis based approaches. The presented material in this chapter is based on the work of Esposito and Floudas (1998).

Keywords

Global Optimization Convex Relaxation Parameter Estimation Problem Global Optimization Approach Deterministic Global Optimization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2000

Authors and Affiliations

  • Christodoulos A. Floudas
    • 1
  1. 1.Department of Chemical EngineeringPrinceton UniversityPrincetonUSA

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