Optimization of distribution functions for theInverse Preisach model by genetic algorithms

In this paper, several identification procedures of the distribution functions of the Preisach model will be investigated by means of a genetic algorithm.The proposed approach has

Four distributions functions, the modified Lorentzian, Guassian, Gauss-Gauss and lognormal- Gauss are used and optimized to simulate the hysteresis loops. In the last step, the

In this paper, a new gene based adaptive mutation scheme is proposed for genetic algorithms (GAs), where the informa- tion on gene based fitness statistics and on gene based

Closed-loop output error identification algorithms with predictors based on generalized orthonormal transfer functions: Convergence conditions and bias distribution.. Bernard Vau,

In spite of this increase in model complexity, examinations on a number of real data sets and known distribution functions put in evidence advantages on the use of these functions

(4) Remark 2 We note that, for bounded and continuous functions f, Lemma 1 can also be derived from the classical Crofton formula, well known in integral geometry (see for

We now apply our main result to the computation of orness and andness average values of internal functions and to the computation of idempotency average values of conjunctive

The expansion bulk speed obtained at 1 AU with the present model, as a function of T e0 and for the three values κ e = 2, κ e = 3 and κ e = 6: One can note that the high speed