Topic
Continuous automaton
About: Continuous automaton is a research topic. Over the lifetime, 947 publications have been published within this topic receiving 17417 citations.
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TL;DR: It is proved that it remains NP-complete even if restricted to Eulerian automata with binary alphabets as it has been conjectured by Martyugin (2011).
Abstract: A word is called a reset word for a deterministic finite automaton if it maps all the states of the automaton to a unique state. Deciding about the existence of a reset word of a given length for a given automaton is known to be an NP-complete problem. We prove that it remains NP-complete even if restricted to Eulerian automata with binary alphabets as it has been conjectured by Martyugin (2011).
4 citations
TL;DR: It is shown that every odometers can be embedded in a gliders-with-reflecting-walls cellular automaton, which one depending on the odometer, and that an odometer can be embedding in a cellular Automaton with local rule xi ↦ xi + xi+1 mod n (i ∈ ℤ), where n depends on the Odometer.
Abstract: We consider the problem of embedding odometers in one-dimensional cellular automata. We show that (1) every odometer can be embedded in a gliders-with-reflecting-walls cellular automaton, which one depending on the odometer, and (2) an odometer can be embedded in a cellular automaton with local rule xi ↦ xi + xi+1 mod n (i ∈ ℤ), where n depends on the odometer, if and only if it is “finitary.”
4 citations
27 Jun 2002
TL;DR: This paper develops a series of learning algorithms for well-known classes of regular languages as instantiations of the same master algorithm, e.g., the learning by rote approach of minimizing the number of states in the automaton and inference of k-reversible languages.
Abstract: Regular language learning from positive examples alone is infeasible Subclasses of regular languages, though, can be inferred from positive examples only The most common approach for learning such is the specific-to-general technique of merging together either states of an initial finite state automaton or nonterminals in a regular grammar until convergenceIn this paper we seek to unify some language learning approaches under the general-to-specific learning scheme In automata terms it is implemented by refining the partition of the states of the automaton starting from one block until desired decomposition is obtained; ie, until all blocks in the partition are uniform according to the predicate determining the properties required from the languageWe develop a series of learning algorithms for well-known classes of regular languages as instantiations of the same master algorithm Through block decomposition we are able to describe in the same scheme, eg, the learning by rote approach of minimizing the number of states in the automaton and inference of k-reversible languagesUnder the worst-case analysis partition-refinement is less efficient than alternative approaches However, for many cases it turns out more efficient in practice Moreover, it ensures the inference of the canonical automaton, whereas the state-merging approach will leave excessive states to the final automaton without a separate minimization step
4 citations
TL;DR: In this paper, the dynamical behavior of the Quantum Cellular Automaton (QCA) is described as a Markov Process, and emergent properties of the discrete dynamical QCA system are defined in the context of the characteristic polynomial of the Markov transition matrix.
Abstract: The dynamical behavior of the Quantum Cellular Automaton (QCA) is described here as a Markov Process. Ergodicity and recurrence, emergent properties of the discrete dynamical QCA system, are defined in the context of the characteristic polynomial of the Markov transition matrix. Except for a few anomalous cases, the transition matrix can be used to predict recurrence times. Finally, a correspondence between recurrence and elementary particle mass is proposed as an example of an emergent property of the QCA system. © 1999 Elsevier Science Ltd. All rights reserved.
4 citations
01 Jan 1996
TL;DR: In this article, the authors proposed a method for generating a discrete event model for a continuous-variable plant from experimental data by measuring sequences of qualitative values of the state variables yield a linear interval matrix equation which can be used to find bounds for the parameters of a linear state space model.
Abstract: The paper proposes a method for generating a discrete event model for a continuous-variable plant from experimental data Measured sequences of qualitative values of the state variables yield a linear interval matrix equation which can be used to find bounds for the parameters of a linear state space model The qualitative model in a form of nondeterministic automaton is then constructed from the identified interval system It can be ensured that the automaton generates all possible qualitative trajectories of the given system The method is illustrated by an application
4 citations