Multi‐innovation gradient‐based iterative identification methods for feedback nonlinear systems by using the decomposition technique

Abstract

This paper studies the parameter estimation problems of feedback nonlinear systems. Combining the multi-innovation identification theory with the negative gradient search, we derive a multi-innovation gradient-based iterative algorithm. In order to reduce the computational burden and further improve the parameter estimation accuracy, a decomposition multi-innovation gradient-based iterative algorithm is proposed by using the decomposition technique. The key is to transform an original system into two subsystems and to estimate the parameters of each subsystem, respectively. A simulation example is provided to demonstrate the effectiveness of the proposed algorithms.

Filtered generalized iterative parameter identification for equation‐error autoregressive models based on the filtering identification idea

“…The proposed iterative algorithms in this article can combine other identification approaches 70–75 to investigate new parameter estimation methods of some stochastic systems with colored noises 76–81 and can be applied to signal processing and chemical process control 82–87 . The calculation amount of the F‐GLSI algorithm in ()–() at each iteration is displayed in Table 2, and the steps of computing the parameter estimates are as follows.…”

Online identification of Hammerstein systems with B‐spline networks

“…The reason why the BS‐MDWSG algorithm can improve the estimation accuracy is that it updates $$$ {\hat{\boldsymbol{\theta}}}_{s1}\left(t-1\right) $$$ and $$$ \hat{\boldsymbol{w}}\left(t-1\right) $$$ using data block by block based a moving data window and the window length is determined by $$$ m $$$ value. The proposed parameter identification algorithms in this article can combine other parameter estimation algorithms 69–74 to explore new parameter estimation methods of different dynamic stochastic systems 75–80 and can be applied to signal processing and chemical process control 81–86 …”

Identification of Multi-Innovation Stochastic Gradients with Maximum Likelihood Algorithm Based on Ship Maneuverability and Wave Peak Models

“…Aiming at the phenomenon of past time-varying parameters in the autoregressive process, with reference to the parameter separation scheme, Xu et al proposed a recursive identification method based on the decomposition technique of interaction estimation theory for estimating the autoregressive coefficients [67]. To reduce the computational burden, Yang et al proposed an iterative algorithm for decomposition based on a multi-innovation gradient using the decomposition technique [68,69]. Considering the identification problem of linear continuous time-lag systems, Sun et al derived a stochastic gradient gradientbased iterative (SG-GI) algorithm capable of estimating unknown parameters and unknown time delays simultaneously by using multi-frequency response and also introduced a forgetting factor to improve the parameter estimation accuracy [70].…”