Algorithm

Identification (biology)

System identification

Mathematical optimization

Mathematical analysis

Artificial intelligence

Economics

Parameterized complexity

Estimation theory

Controller (irrigation)

Control (management)

Convergence (economics)

Mathematics

Computer science

Control theory (sociology)

Economic growth

Botany

Agronomy

Data modeling

Linear system

Database

Biology

LTI system theory

Journal of the Franklin Institute

This paper is concerned with robust identification of Wiener systems in the presence of dynamic disturbances and stochastic noises. Since conventional statistical method cannot eliminate the dynamic disturbance. To solve this problem, the dynamic disturbance signal is modelled as a self-excitation time-varying parameters to be estimated by tracking strategy. Using the multi-innovation and auxiliary model scheme, a hierarchical recursive least squares algorithm with forgetting factors is designed in discrete-time domain. To improve the parameter estimation accuracy and decrease the error variance, the multi-innovation strategy is used to estimate the time-invariant system parameters. The dynamic disturbance still uses the single innovation method for quick tracking. The estimation error upper bound with finite sample data and asymptotic convergence properties are analyzed. Some guidelines are suggested to help the choice of multi-innovation length. The proposed parameterized identification facilitates controller design and system performance analysis. The effectiveness and superiority of the proposed algorithm are confirmed by utilizing theoretical analysis and numerical examples.

Robust hierarchical identification of Wiener systems in the presence of dynamic disturbances