Enhanced Model Identification in Signal Processing Using Arbitrary Exponential Functions

Abstract

A method for finding a probability density function (PDF) and its statistical moments for a chosen one of four newly derived probability models for an arbitrary exponential function of the forms ... . The model chosen will depend on the domain of the data and whether information on the parameters a and b exists. These parameters may typically be the mean or average of the data and the standard deviation, respectively. Non-linear regression analyses are performed on the data distribution and a basis function is reconstructed from the estimates in the final solution set to obtain a PDE, a moment generating function and the mean and variance. Simple hypotheses about the behavior of such functional forms may be tested statistically once the empirical least squares methods have identified an applicable model derived from actual measurements.

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Document Details

Document Type
Technical Report
Publication Date
Mar 27, 2000
Accession Number
ADD019818

Entities

People

  • Bruce J. Bates
  • Chung T. Nguyen
  • Francis J. O'brien

Organizations

  • United States Department of the Navy

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Complex Variables
  • Data Science
  • Differential Equations
  • Equations
  • Exponential Functions
  • Hypotheses
  • Information Processing
  • Information Science
  • Inventions
  • Least Squares Method
  • Mathematics
  • Probability
  • Probability Density Functions
  • Probability Distributions
  • Random Variables
  • Regression Analysis
  • Signal Processing

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Computational Fluid Dynamics (CFD)
  • Regression Analysis.