.png){kind=icon}
.png){kind=icon}
Return
Recursive methods for estimating the radial basis function-based state-dependent autoregressive model
.png){kind=icon}
PRE
AI
DOI：10.1002/rnc.4890.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Identifying a nonlinear radial basis function-based state-dependent autoregressive (RBF-AR) time series model is the basis for solving the corresponding prediction and control problems. This paper studies some recursive parameter estimation algorithms for the RBF-AR model. Considering the difficulty of the nonlinear optimal problem arising in estimating the RBF-AR model, an overall forgetting gradient algorithm is deduced based on the negative gradient search. A numerical method with a forgetting factor is provided to solve the problem of determining the optimal convergence factor. In order to improve the parameter estimation accuracy, the multi-innovation identification theory is applied to develop an overall multi-innovation forgetting gradient (O-MIFG) algorithm. The simulation results indicate that the estimation model based on the O-MIFG algorithm can capture the dynamics of the RBF-AR model very well.
Keywords:
multi-innovation identification
nonlinear time series
parameter estimation
RBF-AR model
recursive search
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.2
Papers:
6.9K
Citations:
1.4W