Combined Linear and Nonlinear Modeling of Data

Abstract

A method is presented for reducing the dimensionality of the search space when some of the unknown parameters appear linearly in the model fit. After elimination of the linear parameters, the gradient vector and the Hessian matrix of the resultant Hermitian form are derived so that an efficient minimization procedure can be developed in multiple dimensions. A 'destabilizing' term is identified in the Hessian matrix and can be dropped from the calculations if desired. This approach is expected to be more reliable; it also does not require any second-order partial derivatives, leading to fewer computations for finding the minimum in the multidimensional search space.

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Document Details

Document Type
Technical Report
Publication Date
Apr 28, 2003
Accession Number
ADA416288

Entities

People

  • Albert H. Nuttall

Organizations

  • Naval Undersea Warfare Center

Tags

Communities of Interest

  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Additives (Chemicals)
  • Amplitude
  • Data Processing
  • Elimination
  • Equations
  • Frequency
  • Geometry
  • Noise
  • Nonlinear Dynamics
  • Numbers
  • Random Variables
  • Sampling
  • Scalar Functions
  • Standards
  • Two Dimensional
  • Undersea Warfare
  • Warfare

Readers

  • Linear Algebra
  • Regression Analysis.

Technology Areas

  • Space