Finite Automaton


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Related to Finite Automaton: Finite state automata

Finite Automaton

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Mei et al.[12] proposed a complex event detection method based on finite automaton, and some of their improved processing methods, for example, Jin et al.[13] proposed a complex event processing method based on timed petri-net.
It consists in the construction of a minimal finite automaton (FA) which can traverse the border of any cluster on any 2D binary grid and always stops in the planar site percolation model.
A deterministic finite automaton ([1.sub.[epsilon]]DFA or 2DFA) can be obtained from 1.sub.[epsilon]NFA or 2NFA by claiming that the transition set H does not allow executing more than one possible transition at a time.
Those models are Finite Automaton model (Kanchana Rajaram and Chitra Babu, 2014) for deriving transactional properties such as compensable, pivot, Retriable.
A deterministic finite automaton ([1.sub.[epsilon]]DFA or 2DFA) can be obtained from [1.sub.[epsilon]]NFA or 2NFA by claiming that the transition set H does not allow executing more than one possible transition at a time.
In diagnosability verification, first, it is proven that if a part of observations in the system can be expressed as a regular language, that is, a language accepted by some finite automaton, then diagnosability is decidable.
A push-down automaton is a finite automaton (nondeterministic) which has a stack, a kind of simple memory in which it can store information in a last-in-first-out fashion.
A finite automaton starts in state [q.sub.0] and processes the input symbols sequentially.
* Definition 2.2 A Deterministic Finite Automaton (DFA) is a 5-tuple A = (Q, [SIGMA], [delta], [q.sub.0], F) where Q is a
Even simple controlling structure (formally represented as a small finite automaton) can create rare combinations of events when acting on inputs without such rare combinations, which suggests that pure testing-based approach cannot prove reliability.
also introduced a kind off fuzzy finite automaton in 1999 [10].
An Hidden Markov Model (HMM) is mathematically equal to a stochastic finite automaton defined by a 5-tuple A = (Q, [summation of], [DELTA], [pi], [OMICRON]) where Q = {[s.sub.0], [s.sub.1], [s.sub.2], [s.sub.3], ..., [s.sub.m]} is finite set of states, [summation of] is an alphabet of output symbols, [DELTA] = {[p.sub.ij]/1 [less than or equal to] i, j [less than or equal to] m} is a state transition probability distribution and [pi] = {[[pi].sub.i]/ 1 [less than or equal to] i [less than or equal to] m} is an initial state distribution, [OMICRON] is the set {[e.sub.j](x)/1 [less than or equal to] j [less than or equal to] n} of output symbol probabilities such that