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zbMATH Open
Article . 2002
Data sources: zbMATH Open
https://doi.org/10.1007/3-540-...
Part of book or chapter of book . 2001 . Peer-reviewed
Data sources: Crossref
DBLP
Article . 2002
Data sources: DBLP
DBLP
Conference object
Data sources: DBLP
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Residual Finite State Automata

Residual finite state automata
Authors: François Denis; Aurélien Lemay; Alain Terlutte;

Residual Finite State Automata

Abstract

We define a new variety of Nondeterministic Finite Automata (NFA): a Residual Finite State Automaton (RFSA) is an NFA all the states of which define residual languages of the language L that it recognizes; a residual language according to a word u is the set of words v such that uv is in L. We prove that every regular language is recognized by a unique (canonical) RFSA which has a minimal number of states and a maximal number of transitions. Canonical RFSAs are based on the notion of prime residual languages, i.e. that are not the union of other residual languages. We provide an algorithmic construction of the canonical RFSA similar to the subset construction used to build the minimal DFA from a given NFA. We study the size of canonical RFSAs and the complexity of our constructions.

Keywords

residual finite state automaton, Formal languages and automata, nondeterministic finite automata

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
30
Top 10%
Top 10%
Average
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