A Learning Algorithm for Deterministic Finite Automata using JFLAP

Adaptive E-learning using Deterministic Finite Automata

Adaptive E-learning refers to adapt the way of presentation of educational material according to the student's needs. Understanding ability differs by student to student so the learning path should vary according to their understanding ability. Some students may understand by once some may needs more with different way. This paper represents a same topic with various approaches to the diff...

An Incremental Learning Algorithm for Deterministic Finite Automata using Evolutionary Algorithms

This work proposes an approach for learning Deterministic Finite Automata (DFA) that combines Incremental Learning and Evolutionary Algorithms. First, the training sequences are sorted according to its length from shortest to longest. Then, the training sequences are divided in a suitable number of groups (M) of the same size. Second, the Hybrid Adaptive Evolutionary Algorithm (HaEa) proposed b...

A polynomial double reversal minimization algorithm for deterministic finite automata

We here propose a polynomial-time deterministic finite automaton minimization algorithm directly derived from Brzozowski’s double reversal algorithm. To do so, we take into account the framework by Brzozowski and Tamm, to propose an atomization algorithm that allows us to achieve polynomial time complexity. © 2013 Elsevier B.V. All rights reserved.

A Practical Algorithm for Reducing Non- deterministic Finite State Automata

In [3], Ilie and Yu describe a construction of a right-invariant equivalence relation on the states of a non-deterministic finite-state automaton. We give a more efficient algorithm for constructing the same equivalence, together with results from a computer implementation.

Learning Regular Languages Using Non Deterministic Finite Automata