Optimal non-linear estimation for distributed-parameter systems via the partition theorem

Keigo Watanabe, Yoshimura Toshio, Takashi Soeda

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

This paper considers the estimation problem for non-linear distributed-parameter systems via the ‘Partition Theorem’. First, the a posterioriprobability for the state is derived for the estimation of non-linear distributed-parameter systems. Secondly, linear systems excited by a white gaussian noise and with non-gaussian initial state are considered as a special class of the problem. The a posterioriprobability for the state, the optimal estimates and corresponding error covariance matrices are obtained by using the properties of the fundamental solution for the differential operator. Finally, it is shown that on approximate expression for the solution of the problem is also derived by applying a gaussian sum approximation technique.

Original languageEnglish
Pages (from-to)1113-1130
Number of pages18
JournalInternational Journal of Systems Science
Volume11
Issue number9
DOIs
Publication statusPublished - 1980
Externally publishedYes

Fingerprint

Nonlinear Estimation
Optimal Estimation
Distributed Parameter Systems
Partition
Covariance matrix
Theorem
Linear systems
Gaussian White Noise
Fundamental Solution
Differential operator
Linear Systems
Approximation
Estimate
Nonlinear estimation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Management Science and Operations Research

Cite this

Optimal non-linear estimation for distributed-parameter systems via the partition theorem. / Watanabe, Keigo; Toshio, Yoshimura; Soeda, Takashi.

In: International Journal of Systems Science, Vol. 11, No. 9, 1980, p. 1113-1130.

Research output: Contribution to journalArticle

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