5,238 research outputs found
Turing machines based on unsharp quantum logic
In this paper, we consider Turing machines based on unsharp quantum logic.
For a lattice-ordered quantum multiple-valued (MV) algebra E, we introduce
E-valued non-deterministic Turing machines (ENTMs) and E-valued deterministic
Turing machines (EDTMs). We discuss different E-valued recursively enumerable
languages from width-first and depth-first recognition. We find that
width-first recognition is equal to or less than depth-first recognition in
general. The equivalence requires an underlying E value lattice to degenerate
into an MV algebra. We also study variants of ENTMs. ENTMs with a classical
initial state and ENTMs with a classical final state have the same power as
ENTMs with quantum initial and final states. In particular, the latter can be
simulated by ENTMs with classical transitions under a certain condition. Using
these findings, we prove that ENTMs are not equivalent to EDTMs and that ENTMs
are more powerful than EDTMs. This is a notable difference from the classical
Turing machines.Comment: In Proceedings QPL 2011, arXiv:1210.029
Supervisory Control of Fuzzy Discrete Event Systems
In order to cope with situations in which a plant's dynamics are not
precisely known, we consider the problem of supervisory control for a class of
discrete event systems modelled by fuzzy automata. The behavior of such
discrete event systems is described by fuzzy languages; the supervisors are
event feedback and can disable only controllable events with any degree. The
concept of discrete event system controllability is thus extended by
incorporating fuzziness. In this new sense, we present a necessary and
sufficient condition for a fuzzy language to be controllable. We also study the
supremal controllable fuzzy sublanguage and the infimal controllable fuzzy
superlanguage when a given pre-specified desired fuzzy language is
uncontrollable. Our framework generalizes that of Ramadge-Wonham and reduces to
Ramadge-Wonham framework when membership grades in all fuzzy languages must be
either 0 or 1. The theoretical development is accompanied by illustrative
numerical examples.Comment: 12 pages, 2 figure
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