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Automatika
Journal for Control, Measurement, Electronics, Computing and Communications
Volume 63, 2022 - Issue 4
462
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Regular Papers

Identification of a nonlinear rational model based on bias compensated multi-innovation stochastic gradient algorithm

ORCID Icon, &
Pages 785-792 | Received 02 Oct 2021, Accepted 06 Jun 2022, Published online: 13 Jun 2022

Figures & data

Figure 1. Block diagram of an ARX-NRM.

Figure 1. Block diagram of an ARX-NRM.

Table 1. Computational costs of the SG, MI-SG, BC-MI-SG, and RLS algorithms.

Figure 2. Curves of the observed data.

Figure 2. Curves of the observed data.

Figure 3. Estimation errors using the SG, MI-SG and BC-MI-SG algorithms.

Figure 3. Estimation errors using the SG, MI-SG and BC-MI-SG algorithms.

Table 2. Estimates using the SG, MI-SG, RLS and BC-MI-SG algorithms.

Figure 4. Estimation errors using BC-MI-SG with different noise variances.

Figure 4. Estimation errors using BC-MI-SG with different noise variances.

Figure 5. Curve of the propylene catalytic oxidation data.

Figure 5. Curve of the propylene catalytic oxidation data.

Table 3. Results using the SG, MI-SG and BC-MI-SG algorithms for the propylene catalytic oxidation data.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.