Learning Algorithm for Fractional Dynamical Systems with Autocorrelated Errors-in-Variables

被引：0

作者：

Ivanov, Dmitriy V. ^{[1
]}

Sandler, Ilya L. ^{[1
]}

Chertykovtseva, Natalya V. ^{[1
]}

Bobkova, Elena U. ^{[2
]}

机构：

[1] Samara State Univ Transport, Dept Mechatron, Samara, Russia

[2] Moscow State Univ Educ, Dept Econ, Moscow, Russia

来源：

PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE OF INFORMATION AND COMMUNICATION TECHNOLOGY [ICICT-2019] | 2019年 / 154卷

关键词：

fractional difference; errors-in-variables; least square; autocorrelated noise; recursive estimation; STOCHASTIC-APPROXIMATION; ORDER SYSTEMS; IDENTIFICATION;

D O I：

10.1016/j.procs.2019.06.045

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper, the stochastic gradient algorithm for learning fractional-order dynamical systems with noisy input and output is proposed. The proposed algorithm allows estimating the parameters of fractional dynamic systems if the input and output noises are color. The proposed algorithm does not require knowledge of the noise distribution laws. The simulation results demonstrate the high accuracy of the proposed learning algorithm in comparison with the least squares learning algorithm. (C) 2019 The Authors. Published by Elsevier Ltd.

引用

页码：311 / 318

页数：8

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