Novel data filtering based parameter identification for multiple-input multiple-output systems using the auxiliary model

IEEE Trans. Syst. Man Cybern, Syst.

et al. 2020

Self Cite

Abstract-A stochastic gradient based particle filter algorithm is developed for an ARX model with nonlinear communication output in this paper. This non-standard ARX model consists of two submodels, one is a linear ARX model and the other is a nonlinear output model. The process outputs (outputs of the linear submodel) transmitted over a communication channel are unmeasureable, while the communication outputs (outputs of the nonlinear submodel) are available, and both of the twotype outputs are contaminated by white noises. Based on the rich input data and the available communication output data, a stochastic gradient based particle filter algorithm is proposed to estimate the unknown process outputs and parameters of the ARX model. Furthermore, a direct weight optimization method and the Epanechnikov kernel method are extended to modify the particle filter when the measurement noise is a Gaussian noise with unknown variance and the measurement noise distribution is unknown. The simulation results demonstrate that the stochastic gradient based particle filter algorithm is effective.

Section: A(d)x(t) = B(d)u(t) + V(t) and The Other Is A Nonlinear Commentioning

confidence: 99%

Gradient-Based Particle Filter Algorithm for an ARX Model With Nonlinear Communication Output

Chen

Liu

IEEE Trans. Syst. Man Cybern, Syst.

et al. 2020

Self Cite

“…This is the drawback of the multivariable RLS algorithm in (6)- (10). This motivates us to study new coupled parameter identification methods.…”

Section: Remarkmentioning

confidence: 99%

“…The idea of the auxiliary model is to use the measurable information to construct a dynamical model and to replace the unknown variables in the information vector with the output of the auxiliary model [10,11]. There are two typical identification methods for multivariate output-error systems: stochastic gradient (SG) algorithms [12,13] and the recursive least squares (RLS) algorithms [14,15].…”

Section: Introductionmentioning

confidence: 99%

Coupled Least Squares Identification Algorithms for Multivariate Output-Error Systems

Huang

2017

Algorithms

Self Cite

This paper focuses on the recursive identification problems for a multivariate output-error system. By decomposing the system into several subsystems and by forming a coupled relationship between the parameter estimation vectors of the subsystems, two coupled auxiliary model based recursive least squares (RLS) algorithms are presented. Moreover, in contrast to the auxiliary model based recursive least squares algorithm, the proposed algorithms provide a reference to improve the identification accuracy of the multivariate output-error system. The simulation results confirm the effectiveness of the proposed algorithms.

“…The multivariable systems contain both parameter vectors and parameter matrices, and the systems inputs and system outputs are relevant and coupled [20][21][22]. For the sake of reducing the computational complexity, the hierarchical identification principle is utilized to transform a complex system into several subsystems and then to estimate the parameter vector of each subsystem [23,24], respectively.…”

Section: Introductionmentioning

confidence: 99%

The Hierarchical Iterative Identification Algorithm for Multi-Input-Output-Error Systems with Autoregressive Noise

2017

Complexity

This paper considers the identification problem of multi-input-output-error autoregressive systems. A hierarchical gradient based iterative (H-GI) algorithm and a hierarchical least squares based iterative (H-LSI) algorithm are presented by using the hierarchical identification principle. A gradient based iterative (GI) algorithm and a least squares based iterative (LSI) algorithm are presented for comparison. The simulation results indicate that the H-LSI algorithm can obtain more accurate parameter estimates than the LSI algorithm, and the H-GI algorithm converges faster than the GI algorithm.