25 research outputs found
Two-Sided Derivatives for Regular Expressions and for Hairpin Expressions
The aim of this paper is to design the polynomial construction of a finite
recognizer for hairpin completions of regular languages. This is achieved by
considering completions as new expression operators and by applying derivation
techniques to the associated extended expressions called hairpin expressions.
More precisely, we extend partial derivation of regular expressions to
two-sided partial derivation of hairpin expressions and we show how to deduce a
recognizer for a hairpin expression from its two-sided derived term automaton,
providing an alternative proof of the fact that hairpin completions of regular
languages are linear context-free.Comment: 28 page
Testing the Equivalence of Regular Languages
The minimal deterministic finite automaton is generally used to determine
regular languages equality. Antimirov and Mosses proposed a rewrite system for
deciding regular expressions equivalence of which Almeida et al. presented an
improved variant. Hopcroft and Karp proposed an almost linear algorithm for
testing the equivalence of two deterministic finite automata that avoids
minimisation. In this paper we improve the best-case running time, present an
extension of this algorithm to non-deterministic finite automata, and establish
a relationship between this algorithm and the one proposed in Almeida et al. We
also present some experimental comparative results. All these algorithms are
closely related with the recent coalgebraic approach to automata proposed by
Rutten
Using neural-computers for improving the control computersβs performance
Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ ΠΈ ΠΏΡΠΈΠ½ΡΠΈΠΏΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ Π½Π΅ΠΉΡΠΎΠ²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»Π΅ΠΉ Π΄Π»Ρ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ Π±ΠΎΡΡΠΎΠ²ΡΡ
Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΠΎΠ΄Π²ΠΈΠΆΠ½ΡΠΌΠΈ ΠΎΠ±ΡΠ΅ΠΊΡΠ°ΠΌΠΈ.an opportunity and principles of using neural-computers for improving the performance of on-board evaluated control-automatic systems of mobile units are propose
Position Automaton Construction for Regular Expressions with Intersection
Positions and derivatives are two essential notions in the conversion methods from regular expressions to equivalent finite automata. Partial derivative based methods have recently been extended to regular expressions with intersection. In this paper, we present a position automaton construction for those expressions. This construction generalizes the notion of position making it compatible with intersection. The resulting automaton is homogeneous and has the partial derivative automaton as its quotient
On the State Complexity of Partial Derivative Automata For Regular Expressions with Intersection
Extended regular expressions (with complement and intersection) are used in many applications due to their succinctness. In particular, regular expressions extended with intersection only (also called semi-extended) can already be exponentially smaller than standard regular expressions or equivalent nondeterministic finite automata (NFA). For practical purposes it is important to study the average behaviour of conversions between these models. In this paper, we focus on the conversion of regular expressions with intersection to nondeterministic finite automata, using partial derivatives and the notion of support. First, we give a tight upper bound of 2O(n) for the worst-case number of states of the resulting partial derivative automaton, where n is the size of the expression. Using the framework of analytic combinatorics, we then establish an upper bound of (1.056 + o(1))n for its asymptotic average-state complexity, which is significantly smaller than the one for the worst case. (c) IFIP International Federation for Information Processing 2016
Consistency and Semantics of Equational Definitions over . . .
We introduce and study the notion of an equational definition over a predefined algebra (EDPA) which is a modification of the notion of an algebraic specification enrichment. We argue that the latter is not quite appropriate when dealing with partial functions (in particular, with those defined by non-terminating functional programs), and suggest EDPA as a more adequate tool for specification and verification purposes. Several results concerning consistency of enrichments and correctness of EDPA are presented. The relations between EDPA and some other approaches to algebraic specification of partial functions are discussed. 1 Introduction 1.1 Motivation Algebraic specification and term-rewriting methods seem very convenient to use in the following wide-spread situation: given a set A of data with several # On leave from the V. M. Glushkov Institute of Cybernetics, Kiev, Ukraine; predefined functions g 1 , . . . , g k on it, one needs to define (sometimes constructively) a set of n..
METHOD FOR RETROSPECTIVE DETERMINATION OF OBJECT MOVEMENT TRAJECTORY AND DEVICE FOR ITS IMPLEMENTATION
FIELD: radar systems. SUBSTANCE: invention relates to radar systems. The method of retrospective determination of the trajectory of an object's movement is characterized by the fact that using a radar receiver of the radar system, radar data about detected objects in previous sounding cycles are collected and stored for a certain period of time, at the initial time t = t0 a standard radar track of the object is formed based on the collected radar data update the standard radar track over a period of time. Each retrospectively built track of the object is assigned a lattice diagram; to obtain the current estimate of the path, only the set of the most plausible paths of the object is used, the most probable path is the path obtained at the current step with the lowest total estimate. A device for retrospective determination of the trajectory of an object has an antenna system associated with a controlled microwave transceiver microcircuit containing a generator connected to any of the n-transmitting antennas, an amplifier, the signal input of which is connected to any of the m-receiving antennas, and the signal output is connected to the first input of the mixer, the second signal input of the mixer is connected to the signal generator. EFFECT: reduced processing time for tracks from various moving objects. 3 cl, 3 dwg.ΠΠ·ΠΎΠ±ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ ΠΎΡΠ½ΠΎΡΠΈΡΡΡ ΠΊ ΡΠ°Π΄Π°ΡΠ½ΡΠΌ ΡΠΈΡΡΠ΅ΠΌΠ°ΠΌ. Π‘ΠΏΠΎΡΠΎΠ± ΡΠ΅ΡΡΠΎΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΠΈ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠ° Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΠ΅ΡΡΡ ΡΠ΅ΠΌ, ΡΡΠΎ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΡΠ°Π΄ΠΈΠΎΠ»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΏΡΠΈΠ΅ΠΌΠ½ΠΈΠΊΠ° ΡΠ°Π΄ΠΈΠΎΠ»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ ΡΠΎΠ±ΠΈΡΠ°ΡΡ ΠΈ Ρ
ΡΠ°Π½ΡΡ ΡΠ°Π΄ΠΈΠΎΠ»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΠ΅ Π΄Π°Π½Π½ΡΠ΅ ΠΎ Π΄Π΅ΡΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΠΎΠ±ΡΠ΅ΠΊΡΠ°Ρ
Π² ΠΏΡΠ΅Π΄ΡΠ΄ΡΡΠΈΡ
ΡΠΈΠΊΠ»Π°Ρ
Π·ΠΎΠ½Π΄ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π·Π° Π½Π΅ΠΊΠΎΡΠΎΡΡΠΉ ΠΏΡΠΎΠΌΠ΅ΠΆΡΡΠΎΠΊ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ, Π² Π½Π°ΡΠ°Π»ΡΠ½ΡΠΉ ΠΌΠΎΠΌΠ΅Π½Ρ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ t=t0 ΡΠΎΡΠΌΠΈΡΡΡΡ ΡΡΠ°Π½Π΄Π°ΡΡΠ½ΡΠΉ ΡΠ°Π΄ΠΈΠΎΠ»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΠΉ ΡΡΠ΅ΠΊ ΠΎΠ±ΡΠ΅ΠΊΡΠ° Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠΎΠ±ΡΠ°Π½Π½ΡΡ
ΡΠ°Π΄ΠΈΠΎΠ»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΡ
Π΄Π°Π½Π½ΡΡ
, ΠΎΠ±Π½ΠΎΠ²Π»ΡΡΡ ΡΡΠ°Π½Π΄Π°ΡΡΠ½ΡΠΉ ΡΠ°Π΄ΠΈΠΎΠ»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΠΉ ΡΡΠ΅ΠΊ Π² ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΠ³ΠΎ ΠΏΠ΅ΡΠΈΠΎΠ΄Π° Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ. ΠΠ°ΠΆΠ΄ΠΎΠΌΡ Π²ΡΡΡΡΠ°ΠΈΠ²Π°Π΅ΠΌΠΎΠΌΡ ΡΠ΅ΡΡΠΎΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΠΎ ΡΡΠ΅ΠΊΡ ΠΎΠ±ΡΠ΅ΠΊΡΠ° ΡΡΠ°Π²ΡΡ Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠ΅ ΡΠ΅ΡΠ΅ΡΡΠ°ΡΡΡ Π΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ, Π΄Π»Ρ ΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΡ ΡΠ΅ΠΊΡΡΠ΅ΠΉ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΏΡΡΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡ ΡΠΎΠ»ΡΠΊΠΎ Π½Π°Π±ΠΎΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΏΡΠ°Π²Π΄ΠΎΠΏΠΎΠ΄ΠΎΠ±Π½ΡΡ
ΠΏΡΡΠ΅ΠΉ ΠΎΠ±ΡΠ΅ΠΊΡΠ°, Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π²Π΅ΡΠΎΡΡΠ½ΡΠΌ ΠΏΡΡΡΠΌ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΠΉ Π½Π° ΡΠ΅ΠΊΡΡΠ΅ΠΌ ΡΠ°Π³Π΅ ΠΏΡΡΡ, ΠΈΠΌΠ΅ΡΡΠΈΠΉ Π½Π°ΠΈΠΌΠ΅Π½ΡΡΡΡ ΡΡΠΌΠΌΠ°ΡΠ½ΡΡ ΠΎΡΠ΅Π½ΠΊΡ. Π£ΡΡΡΠΎΠΉΡΡΠ²ΠΎ Π΄Π»Ρ ΡΠ΅ΡΡΠΎΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΠΈ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠ° ΡΠΎΠ΄Π΅ΡΠΆΠΈΡ Π°Π½ΡΠ΅Π½Π½ΡΡ ΡΠΈΡΡΠ΅ΠΌΡ, ΡΠ²ΡΠ·Π°Π½Π½ΡΡ Ρ ΡΠΏΡΠ°Π²Π»ΡΠ΅ΠΌΠΎΠΉ ΠΌΠΈΠΊΡΠΎΡΡ
Π΅ΠΌΠΎΠΉ Π‘ΠΠ§-ΠΏΡΠΈΡΠΌΠΎΠΏΠ΅ΡΠ΅Π΄Π°ΡΡΠΈΠΊΠ°, ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠ΅ΠΉ Π³Π΅Π½Π΅ΡΠ°ΡΠΎΡ, ΠΏΠΎΠ΄ΠΊΠ»ΡΡΠ°Π΅ΠΌΡΠΉ ΠΊ Π»ΡΠ±ΠΎΠΉ ΠΈΠ· n-ΠΏΠ΅ΡΠ΅Π΄Π°ΡΡΠΈΡ
Π°Π½ΡΠ΅Π½Π½, ΡΡΠΈΠ»ΠΈΡΠ΅Π»Ρ, ΡΠΈΠ³Π½Π°Π»ΡΠ½ΡΠΉ Π²Ρ
ΠΎΠ΄ ΠΊΠΎΡΠΎΡΠΎΠ³ΠΎ ΠΏΠΎΠ΄ΠΊΠ»ΡΡΠ°Π΅ΡΡΡ ΠΊ Π»ΡΠ±ΠΎΠΉ ΠΈΠ· m-ΠΏΡΠΈΠ΅ΠΌΠ½ΡΡ
Π°Π½ΡΠ΅Π½Π½, Π° ΡΠΈΠ³Π½Π°Π»ΡΠ½ΡΠΉ Π²ΡΡ
ΠΎΠ΄ ΠΏΠΎΠ΄ΠΊΠ»ΡΡΠ΅Π½ ΠΊ ΠΏΠ΅ΡΠ²ΠΎΠΌΡ Π²Ρ
ΠΎΠ΄Ρ ΡΠΌΠ΅ΡΠΈΡΠ΅Π»Ρ, Π²ΡΠΎΡΠΎΠΉ ΡΠΈΠ³Π½Π°Π»ΡΠ½ΡΠΉ Π²Ρ
ΠΎΠ΄ ΡΠΌΠ΅ΡΠΈΡΠ΅Π»Ρ ΠΏΠΎΠ΄ΠΊΠ»ΡΡΠ΅Π½ ΠΊ Π³Π΅Π½Π΅ΡΠ°ΡΠΎΡΡ ΡΠΈΠ³Π½Π°Π»Π°. ΠΠΎΡΡΠΈΠ³Π°Π΅ΡΡΡ ΡΠΎΠΊΡΠ°ΡΠ΅Π½ΠΈΠ΅ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΡΠ΅ΠΊΠΎΠ² ΠΎΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΠΏΠΎΠ΄Π²ΠΈΠΆΠ½ΡΡ
ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ². 2 Π½. ΠΈ 1 Π·.ΠΏ. Ρ-Π»Ρ, 3 ΠΈΠ»