Algorithm

Control (management)

Process (computing)

Linear system

Biology

Identification (biology)

Statistics

Convergence (economics)

Autoregressive–moving-average model

Operating system

Economics

Estimation theory

Computer science

Botany

Database

Mathematics

Image (mathematics)

Autoregressive model

System identification

Mathematical analysis

Artificial intelligence

Moving average

Economic growth

Control theory (sociology)

Noise (video)

Iterative method

Data modeling

IET Control Theory and Applications

This study considers the parameter identification problem of a pseudo-linear autoregressive moving average system (i.e. linear-in-parameter autoregressive output-error ARMA systems), whose disturbance is an ARMA process. By means of the filtering technique, a filtering-based gradient iterative (F-GI) algorithm and a filtering-based least squares iterative (LSI) algorithms are presented for enhancing the estimation accuracy. Furthermore, a filtering-based decomposition LSI algorithm is derived for improving the computational efficiency. The key is to use the hierarchical identification principle, to apply the data filtering technique for identification, and to replace the unknown terms in the information vectors with their estimates. Compared with the F-GI algorithm, the filtering-based LSI algorithm and the filtering-based decomposition LSI algorithm have faster convergence rates. The simulation results indicate that the proposed algorithms are effective.

Iterative parameter identification for pseudo‐linear systems with ARMA noise using the filtering technique