On the Synthesis of Finite-State Machines from Samples of Their Behavior

The Nerode realization technique for synthesizing finite-state machines from their associated right-invariant equivalence relations is modified to give a method for synthesizing machines from finite subsets of their input-output behavior. The synthesis procedure includes a parameter that one may adj...

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Bibliographic Details
Published inIEEE transactions on computers Vol. C-21; no. 6; pp. 592 - 597
Main Authors Biermann, A. W., Feldman, J. A.
Format Journal Article
LanguageEnglish
Published IEEE 01.06.1972
Subjects
Complexity theory
Computers
Construction industry
Data mining
Finite-state functions
finite-state machines
inference
Nerode realization
Probability density function
sequential learning
Silicon
synthesis
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ISSN0018-9340
DOI10.1109/TC.1972.5009015

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Summary:The Nerode realization technique for synthesizing finite-state machines from their associated right-invariant equivalence relations is modified to give a method for synthesizing machines from finite subsets of their input-output behavior. The synthesis procedure includes a parameter that one may adjust to obtain machines that represent the desired behavior with varying degrees of accuracy and that consequently have varying complexities. We discuss some of the uses of the method, including an application to a sequential learning problem.
ISSN:0018-9340
DOI:10.1109/TC.1972.5009015