Adaptive filter

Statistics

Algorithm

Identification (biology)

System identification

Physics

Mathematical analysis

Recursive least squares filter

Artificial intelligence

Nonlinear system

Autoregressive model

Estimation theory

Variable (mathematics)

Control (management)

Mathematics

Computer science

Control theory (sociology)

Botany

Quantum mechanics

Data modeling

Autoregressive–moving-average model

Database

Biology

International Journal of Adaptive Control and Signal Processing

Summary For the parameter estimation problem of an input nonlinear controlled autoregressive moving average system with variable‐gain nonlinearity, this article gives an analytical form of the variable‐gain nonlinearity by introducing an appropriate switching function and derives an auxiliary model‐based extended stochastic gradient algorithm with a forgetting factor and an auxiliary model‐based recursive extended least‐squares algorithm. For the sake of improving the parameter estimation accuracy, an auxiliary model‐based multi‐innovation extended stochastic gradient algorithm with a forgetting factor and an auxiliary model‐based multi‐innovation recursive extended least‐squares algorithm are presented by utilizing the multi‐innovation identification theory. The simulation results confirm the effectiveness of the proposed algorithms and show that the auxiliary model‐based multi‐innovation recursive identification algorithms have higher identification accuracy compared with the other two algorithms.

Auxiliary model‐based multi‐innovation recursive identification algorithms for an input nonlinear controlled autoregressive moving average system with variable‐gain nonlinearity