Sh. R. Nurutdinov
Implementation of Markov chains over Galois fields
The automaton implementation of a determinate function is a probabilistic automaton A
1 = (S, Y, Ps, λ(s)), where S is the Markov chain state set, Ps is an m1 × m1 stochastic matrix, Y is the output alphabet of cardinality m2 ≤ m1. The automaton implementation of a probabilistic function is a probabilistic automaton A2 = (S, Y, Ps, Py), where S, Y, Ps are of the same sense as in A1, while Py is a stochastic m1 × m2 matrix.
In this paper, we solve the problem of synthesis of generators of finite homogeneous Markov chains on the base of the analytical apparatus of polynomial functions over a Galois field. We suggest a method to calculate the coefficients of a polynomial in several variables which implements any mapping of the Galois field into itself. We study the case of implementing a finite automaton by a homogeneous computing structure defined over a Galois field; automaton mappings are implemented as polynomial functions over the Galois field. As the base polynomials, we use polynomial functions over the Galois field.
As the base polynomials, we use polynomial functions over the Galois field
where r = 2n – 1, x, s, bi, aij ∈ GF(2n).
We give expressions of an automaton A1 in the framework of a polynomial model over the field GF(2n) of the form M1 = (, f1(x, s), f2(s)), where is the discrete random variable which takes values µ ∈ GF(2n) determined by some probability vector = (p1, . . . , pk1 ) such that
where Bi are stochastic Boolean matrices and k1 ≥ – m1 + 1, and of an automaton M2 = (, f1(x, s), f2(s), , f3(x, s)), where is a discrete random variable which takes values µ′ ∈ GF(2n) determined by some probability vector = (p1, . . . , pk2) such that
where Bi are stochastic Boolean matrices and k2 ≥ – m1 + 1. The problem of representation of
a discrete random variable over the field GF(2n) has been solved earlier.
Discrete Mathematics and Applications, Walter de Gruyter
Print ISSN: 0924-9266
Volume: 14, 07/2004
Pages: 273 - 285
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