3 research outputs found
Minimization of Reactive Probabilistic Automata
The problem of finite automata minimization is important for software and hardware designing. Different
types of automata are used for modeling systems or machines with finite number of states. The limitation of
number of states gives savings in resources and time. In this article we show specific type of probabilistic
automata: the reactive probabilistic finite automata with accepting states (in brief the reactive probabilistic
automata), and definitions of languages accepted by it. We present definition of bisimulation relation for
automata's states and define relation of indistinguishableness of automata states, on base of which we could
effectuate automata minimization. Next we present detailed algorithm reactive probabilistic automata’s
minimization with determination of its complexity and analyse example solved with help of this algorithm
A Fuzzy Petri Nets Model for Computing With Words
Motivated by Zadeh's paradigm of computing with words rather than numbers,
several formal models of computing with words have recently been proposed.
These models are based on automata and thus are not well-suited for concurrent
computing. In this paper, we incorporate the well-known model of concurrent
computing, Petri nets, together with fuzzy set theory and thereby establish a
concurrency model of computing with words--fuzzy Petri nets for computing with
words (FPNCWs). The new feature of such fuzzy Petri nets is that the labels of
transitions are some special words modeled by fuzzy sets. By employing the
methodology of fuzzy reasoning, we give a faithful extension of an FPNCW which
makes it possible for computing with more words. The language expressiveness of
the two formal models of computing with words, fuzzy automata for computing
with words and FPNCWs, is compared as well. A few small examples are provided
to illustrate the theoretical development.Comment: double columns 14 pages, 8 figure
Probabilistic automata for computing with words
Usually, probabilistic automata and probabilistic grammars have crisp symbols as inputs, which can be viewed as the formal models of computing with values. In this paper, we first introduce probabilistic automata and probabilistic grammars for computing with (some special) words, where the words are interpreted as probabilistic distributions or possibility distributions over a set of crisp symbols. By probabilistic conditioning, we then establish a retraction principle from computing with words to computing with values for handling crisp inputs and a generalized extension principle from computing with words to computing with all words for handling arbitrary inputs. These principles show that computing with values and computing with all words can be respectively implemented by computing with some special words. To compare the transition probabilities of two near inputs, we also examine some analytical properties of the transition probability functions of generalized extensions. Moreover, the retractions and the generalized extensions are shown to be equivalence-preserving. Finally, we clarify some relationships among the retractions, the generalized extensions, and the extensions studied by Qiu and Wang. © 2012 Elsevier Inc