Modelling and Identification of Relatively Slowly Varying Systems.

Abstract

This thesis is concerned with the modeling and identification of a large class of nonlinear, time-varying, causal, bounded memory systems. A generic model is developed for such time-varying systems, and this model is formulated in terms of a finite set of parameters which adequately describe the unknown system mapping. Two separate models are considered for the variation in the parameters representing the unknown system. In the first, the variation is assumed to be unknown but bounded between measurement times, and in the second, the variation between measurement times is taken to be random, with known mean and covariance. In each case, two methods of processing the observations are considered.

Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1975
Accession Number
ADA014002

Entities

People

  • Philip Howard Fiske

Organizations

  • University of Michigan

Tags

DTIC Thesaurus Topics

  • Acquisition
  • Computing-Related Activities
  • Covariance
  • Data Acquisition
  • Data Science
  • Identification
  • Information Processing
  • Information Science
  • Interdisciplinary Science
  • Mathematical Analysis
  • Measurement
  • Observation

Fields of Study

  • Mathematics

Readers

  • Acoustical Oceanography.
  • Atmospheric Science / Meteorology, specifically Wind Wave Turbulence.
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms