The αBB Approach in Parameter Estimation
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).
KeywordsGlobal Optimization Convex Relaxation Parameter Estimation Problem Global Optimization Approach Deterministic Global Optimization
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