Some uniform categorical theoretical treatment of automata and lattice-valued fuzzy automata using quantale theory is studied in this paper. First, L-relational sheaves on a monoidM andQ-enriched categories are introduced for quantales L andQ, the equivalence of the corresponding categories are proved next. Then lattice-valued (fuzzy) automata are described by Q-enriched categories. In fact, la...

Cellular learning automata is a combination of learning automata and cellular automata. This model is superior to cellular learning automata because of its ability to learn and also is superior to single learning automaton because it is a collection of learning automata which can interact together. In some applications such as image processing, a type of cellular learning automata in which the ...

We consider co–Büchi tree automata along with both alternating and generalized paradigms, as a characterization of the class of languages whose complement is accepted by generalized Büchi tree automata. We first prove that for alternating generalized co–Büchi tree automata the simulation theorem does not hold and the generalized acceptance does not add to the expressive power of the model. Then...

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The purpose of this paper is to describe structural properties of automata whose transition semigroups have a zero, left zero, right zero or bi-zero, or are nilpotent extensions of rectangular bands, left zero bands or right zero bands, or are nilpotent. To describe the structure of these automata we use various well-known decomposition methods of automata theory – direct sum decompositions, su...

Abstract In 2021, Casares, Colcombet, and Fijalkow introduced the Alternating Cycle Decomposition (ACD) to study properties transformations of Muller automata. We present first practical implementation ACD in two different tools, Owl Spot, adapt it framework Emerson-Lei automata, i.e., $$\omega $$ ω -automata...

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...

In a previous work, we showed that a kind of ω-automata known as Transition-based Generalized Testing Automata (TGTA) can outperform the Büchi automata traditionally used for explicit model checking when verifying stutter-invariant properties. In this work, we investigate the use of these generalized testing automata to improve symbolic model checking of stutter-invariant LTL properties. We pro...

Weak convergence methods are used to analyse generalized learning automata algorithms. The REINFORCE algorithm has been analysed. It is shown by an example that this algorithm can exhibit unbounded behaviour. A modification based on constrained optimization principles is proposed to overcome this problem. The relationship between the asymptotic behaviour of the modified algorithm and the Kuhn-T...

Minimum Steiner tree problem in a weighted graph is to find a least cost sub tree in the graph such that it contains special vertices called terminal points. Since this problem belongs to NP-Complete problems, many approximate algorithms including genetic algorithms and ant colony strategies have been designed for solving it. In this paper, we propose an algorithm based on distributed learning ...