| dc.creator | Giesbrecht | |
| dc.creator | Mateus; Bottura | |
| dc.creator | Celso Pascoli | |
| dc.date | 2017 | |
| dc.date | mar | |
| dc.date | 2017-11-13T13:12:16Z | |
| dc.date | 2017-11-13T13:12:16Z | |
| dc.date.accessioned | 2018-03-29T05:50:31Z | |
| dc.date.available | 2018-03-29T05:50:31Z | |
| dc.identifier | Expert Systems With Applications. Pergamon-elsevier Science Ltd , v. 69, p. 189 - 200, 2017. | |
| dc.identifier | 0957-4174 | |
| dc.identifier | 1873-6793 | |
| dc.identifier | WOS:000389111000017 | |
| dc.identifier | 10.1016/j.eswa.2016.10.023 | |
| dc.identifier | http://www-sciencedirect-com.ez88.periodicos.capes.gov.br/science/article/pii/S0957417416305541?via%3Dihub | |
| dc.identifier | http://repositorio.unicamp.br/jspui/handle/REPOSIP/326840 | |
| dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/1363865 | |
| dc.description | In this paper an immuno-inspired algorithm is proposed to generate sequences of data close to the ideal white noise. The motivation to propose the algorithm is that in many cases there is a necessity to generate white noises with a small number of samples, and the pseudo-random generators may fail to perform this task. The proposed algorithm is based on the maximization of the whiteness criterion, clearly defined in this paper, and is different from other immuno-inspired algorithms because it presents an automatic regulation of the suppression threshold, that is an important control variable of the algorithm. This feature allows the proposed algorithm to reach good results, even for different sizes of candidate solutions. To test the proposed algorithm, the results obtained from it are compared to the results obtained from a known pseudo-random generator and it is shown that the solutions obtained with the proposed algorithm are better, for time and frequency domain, if the number of samples required is limited. It is also shown that the proposed algorithm consumes a time comparable to the pseudo-random generator to reach solutions that are better than the ones obtained with that kind of algorithm. (C) 2016 Elsevier Ltd. All rights reserved. | |
| dc.description | 69 | |
| dc.description | 189 | |
| dc.description | 200 | |
| dc.language | English | |
| dc.publisher | Pergamon-Elsevier Science LTD | |
| dc.publisher | Oxford | |
| dc.relation | Expert Systems with Applications | |
| dc.rights | fechado | |
| dc.source | WOS | |
| dc.subject | White Noise | |
| dc.subject | White Noise Generation | |
| dc.subject | Immuno-inspired Algorithms | |
| dc.title | Finite Length White Noise Generation With An Immuno Inspired Algorithm | |
| dc.type | Artículos de revistas | |
