2011IEEE Transactions on Information TheoryRequires access

Minimal Realizations of Linear Systems: The “Shortest Basis” Approach

G. David Forney

Open publisher page 23 citations

Abstract

Given a discrete-time linear systemC, a shortest basis forCis a set of linearly independent generators forCwith the least possible lengths. A basisBis a shortest basis if and only if it has the predictable span property (i.e., has the predictable delay and degree properties, and is non-catastrophic), or alternatively if and only if it has the subsystem basis property (for any intervalJ, the generators inBwhose span is inJis a basis for the subsystemCJ). The dimensions of the minimal state spaces and minimal transition spaces ofCare simply the numbers of generators in a shortest basisBthat are active at any given state or symbol time, respectively. A minimal linear realization forCin controller canonical form follows directly from a shortest basis forC, and a minimal linear realization forCin observer canonical form follows directly from a shortest basis for the orthogonal systemC⊥. This approach seems conceptually simpler than that of classical minimal realization theory.

About this research paper

What this paper is about

Given a discrete-time linear systemC, a shortest basis forCis a set of linearly independent generators forCwith the least possible lengths. A basisBis a shortest basis if and only if it has the predictable span property (i.e., has the predictable delay and degree properties, and is non-catastrophic), or alternatively if and only if it has the subsystem basis property (for any intervalJ, the generators inBwhose span is inJis a basis for the subsystemCJ). The dimensions of the minimal state spaces and minimal transition spaces ofCare simply the numbers of generators in a shortest basisBthat are active at any given state or symbol time, respectively. A minimal linear realization forCin controller canonical form follows directly from a shortest basis forC, and a minimal linear realization forCin observer canonical form follows directly from a shortest basis for the orthogonal systemC⊥. This approach seems conceptually simpler than that of classical minimal realization theory.

Why it matters

OpenAlex reports 23 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Given a discrete-time linear systemC, a shortest basis forCis a set of linearly independent generators forCwith the least possible lengths. A basisBis a shortest basis if and only if it has the predictable span property (i.e., has the predictable delay and degree properties, and is non-catastrophic), or alternatively if and only if it has the subsystem basis property (for any intervalJ, the generators inBwhose span is inJis a basis for the subsystemCJ). The dimensions of the minimal state spaces and minimal transition spaces ofCare simply the numbers of generators in a shortest basisBthat are active at any given state or symbol time, respectively. A minimal linear realization forCin controller canonical form follows directly from a shortest basis forC, and a minimal linear realization forCin observer canonical form follows directly from a shortest basis for the orthogonal systemC⊥. This approach seems conceptually simpler than that of classical minimal realization theory.

Key concepts: Basis (linear algebra), Set (abstract data type), Computer science, Combinatorics, Algorithm, Discrete mathematics, Mathematics, Programming language

Related papers

Back to paper searchBrowse research topicsOriginal source
Minimal Realizations of Linear Systems: The “Shortest Basis” Approach — Research Paper | ScholarLens