An Algorithm for Computing Derivatives of Noisy Data

Abstract

An algorithm for computing derivatives of noisy data, as described by the authors in two previous reports, is modified, and empirical support for the choice of relative weights for approximating functions is provided. The algorithm is applied to ten sets of synthetic data generated by evaluating analytic functions and addition pseudo-random errors to their values. The accuracy of the estimates of the first and second derivatives is on the average almost five times better than that obtained by cubic B-spline approximation. Extrapolation is considered.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1983
Accession Number
ADA126127

Entities

People

  • Ceslovas Masaitis
  • George Francis

Organizations

  • Ballistic Research Laboratory

Tags

Communities of Interest

  • C4I
  • Ground and Sea Platforms
  • Materials and Manufacturing Processes
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Analytic Functions
  • Army
  • Army Training
  • Artillery
  • Computer Programs
  • Computers
  • Data Sets
  • Eigenvalues
  • Equations
  • Mathematical Filters
  • Procedures (Computers)
  • Standards
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  • Test And Evaluation
  • United States

Fields of Study

  • Computer science

Readers

  • Approximation Theory.