Vector space formulation of probabilistic finite state automata

Yicheng Wen, Asok Ray

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

This paper develops a vector space model of a class of probabilistic finite state automata (PFSA) that are constructed from finite-length symbol sequences. The vector space is constructed over the real field, where the algebraic operations of vector addition and the associated scalar multiplication operations are defined on a probability measure space, and implications of these algebraic operations are interpreted. The zero element of this vector space is semantically equivalent to a PFSA, referred to as symbolic white noise. A norm is introduced on the vector space of PFSA, which provides a measure of the information content. An application example is presented in the framework of pattern recognition for identification of robot motion in a laboratory environment.

Original languageEnglish (US)
Pages (from-to)1127-1141
Number of pages15
JournalJournal of Computer and System Sciences
Volume78
Issue number4
DOIs
StatePublished - Jul 2012

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Networks and Communications
  • Computational Theory and Mathematics
  • Applied Mathematics

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