Generalized rough and fuzzy rough automata for semantic computing

Abstract

The classical automata, fuzzy finite automata, and rough finite state automata are some formal models of computing used to perform the task of computation and are considered to be the input device. These computational models are valid only for fixed input alphabets for which they are defined and, therefore, are less user-friendly and have limited applications. The semantic computing techniques provide a way to redefine them to improve their scope and applicability. In this paper, the concept of semantically equivalent concepts and semantically related concepts in information about real-world applications datasets are used to introduce and study two new formal models of computations with semantic computing (SC), namely, a rough finite-state automaton for SC and a fuzzy finite rough automaton for SC as extensions of rough finite-state automaton and fuzzy finite-state automaton, respectively, in two different ways. The traditional rough finite-state automata can not deal with situations when external alphabet or semantically equivalent concepts are given as inputs. The proposed rough finite-state automaton for SC can handle such situations and accept such inputs and is shown to have successful real-world applications. Similarly, a fuzzy finite rough automaton corresponding to a fuzzy automaton is also failed to process input alphabet different from their input alphabet, the proposed fuzzy finite rough automaton for SC corresponding to a given fuzzy finite automaton is capable of processing semantically related input, and external input alphabet information from the dataset obtained by real-world applications and provide better user experience and applicability as compared to classical fuzzy finite rough automaton.

Rough Pushdown Automata and Rough Context Free Grammar

“…Pal et al introduced L-fuzzy rough automata and L-fuzzy rough language corresponding to an L-fuzzy automaton, investigating the relationship between definite/possible L-fuzzy languages accepted by L-fuzzy rough automata [7]. Yadav et al presented generalized rough finite-state automata and fuzzy finite rough automata for semantic computing (SC) capable of accepting semantically equivalent incomplete, vague, and insufficient input information from real-world applications [16]. In [6], Kierczak defined the notions of best lower and upper approximations of grammar, along with properties of languages generated by these approximations.…”

Fuzzy Multiset Finite Automata: Some Algebraic and Lattice Theoretic Characterizations

“…The algebraic views of FFSA have been studied by Mordeson and Malik [19,20] and Jin [21] whereas lattice theoretic aspects of FFSA have been studied in Tiwari, Yadav, and Singh [22]. The computational model rough finite state automaton was introduced by Basu [23] to model systems with insufficient and incomplete data set obtained by real-world applications and is further generalized by Yadav, Tiwari, Mausam and Yadav [24] and shown to have real-world application of model.…”