Auxiliary model multiinnovation stochastic gradient parameter estimation methods for nonlinear sandwich systems

Summary For a dual‐rate sampled‐data stochastic system with additive colored noise, a dual‐rate identification model is obtained by using the polynomial transformation technique, which is suitable ...

Maximum likelihood hierarchical least squares‐based iterative identification for dual‐rate stochastic systems

By combining the coupling identification concept with the gradient search, this study develops a partially coupled generalised extended projection algorithm and a partially coupled generalised extended stochastic gradient algorithm to estimate the parameters of a multivariable output-error-like system with autoregressive moving average noise from input–output data The key is to divide the identification model into several submodels based on the hierarchical identification principle and to establish the parameter estimation algorithm by using the coupled relationship between these submodels The simulation test results indicate that the proposed algorithms are effective

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-spr.2019.0481

Recursive coupled projection algorithms for multivariable output-error-like systems with coloured noises

Radial basis function-based state-dependent autoregressive (RBF-AR) models are a class of nonlinear combined models. This letter focuses on the parameter estimation for the RBF-AR models. To overcome the estimation difficulty due to the highly nonlinear relations between the parameters and the model output, the separated idea is used to transform the original optimization problem into a quadratic and a nonlinear optimization problems. Applying the hierarchical identification principle and the multi-innovation theory, two interactive algorithms are proposed for the RBF-AR models. In addition, an approach based on data weighting is proposed to overcome the data saturation in the algorithms. The simulation results verify the effectiveness of the proposed algorithms from the aspects of parameter estimation accuracy and prediction performance.

Modeling Nonlinear Processes Using the Radial Basis Function-Based State-Dependent Autoregressive Models

Three-stage forgetting factor stochastic gradient parameter estimation methods for a class of nonlinear systems

Maximum likelihood methods are based on the probability and statistics theory, and significant for parameter estimation and system modeling. This paper combines the maximum likelihood principle with the data filtering technique for parameter estimation of a class of bilinear systems. The input-output representation of a bilinear system is derived through eliminating the state variables in the model. Then, a filtering based maximum likelihood iterative least squares algorithm is proposed for identifying the parameters of bilinear systems with colored noises by filtering the input-output data with a filter. A least squares based iterative algorithm is given for comparison. The simulation results indicate that the proposed algorithm is effective for identifying bilinear systems. The filtering based maximum likelihood iterative least squares algorithm is more accurate under different noise variance, and has higher computational efficiency.

Meihang Li

Papers

Maximum Likelihood Least Squares Based Iterative Estimation for a Class of Bilinear Systems Using the Data Filtering Technique