State filtering and parameter estimation for two‐input two‐output systems with time delay

Gu, Yu; Zhu, Quanmin; Li, Chuanjiang; Zhu, Peiyi; Nouri, Hassan

doi:10.1049/cth2.12161

Cited by 11 publications

(5 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed parameter estimation algorithms in this article are based on this identification model in (1). Many identification methods are derived based on the identification models of the systems [40][41][42][43][44][45][46] and can be used to estimate the parameters of other linear systems and nonlinear systems [47][48][49][50] and can be applied to fields [51][52][53] such as chemical process control systems. The process of calculating the estimateŝ(t) andb(t) of the weight vector and the bias term b through the NR-SVM algorithm ( 17)-( 27) is shown in Table 1.…”

Section: Initialmentioning

confidence: 99%

Multi‐innovation Newton recursive methods for solving the support vector machine regression problems

Ding

Wang

2021

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

The support vector machine has been widely used in binary classification applications because of the simplicity of its implementation. This article proposes an online identification method based on the support vector machine in the field of parameter identification. By substituting the constraint item into the original criterion function to form a new criterion function about the weight vector and the bias item, and then on the basis of the recursive identification methods, the newly established criterion function is minimized by using the Newton search to derive the Newton recursive support vector machine algorithm. Additionally, in order to improve the performance of the Newton recursive support vector machine algorithm, the multi-innovation identification theory is applied to optimize the algorithm, and a multi-innovation Newton recursive support vector machine algorithm is derived. In addition, the convergence and the calculation amount of the proposed algorithms are carefully analyzed in this article.Finally, a numerical simulation example is given to compare the Newton recursive algorithm and the corresponding multi-innovation recursive algorithm. The results show that the algorithms and optimization strategy studied in this article are effective.

show abstract

Section: Initialmentioning

confidence: 99%

Multi‐innovation Newton recursive methods for solving the support vector machine regression problems

Ding

Wang

2021

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…In Reference [28], a KF based iterative LS algorithm was presented. Ya et al, 29 propose a two‐steps joint parameter and state estimation based on the LS identification algorithm for multivariable systems on canonical state space models. In Reference [30] based on KF a hierarchical multi‐innovation stochastic gradient was proposed to joint estimate the parameters and states of bilinear systems.…”

Section: Introductionmentioning

confidence: 99%

Distributed joint parameter and state estimation algorithm for large‐scale interconnected systems

Hamdi,

Kamoun,

Idoumghar

et al. 2024

Adaptive Control & Signal

View full text Add to dashboard Cite

SummaryThis paper proposes a distributed joint parameter and state variables estimation algorithm for large‐scale state‐space interconnected systems. In this distributed estimation scheme, each interconnected sub‐system is described by a linear discrete‐time state space mathematical model. Each sub‐system is supposed to be controlled by an intelligent controller that can communicate with its interconnected neighbors and exchange information, such as state variables. The proposed approach comprises two recursive estimation algorithms, a parameter estimation algorithm considering the state space model and a distributed Kalman filter for state variables estimation. It is a fully distributed cooperative approach that allows to reduce complexity and saves computational and communication resources. Theoretical analysis and numerical examples are provided to prove the feasibility and effectiveness of this joint estimation algorithm.

show abstract

“…[5][6][7][8] Some methods are for scalar systems [9][10][11][12] and some methods are for multivariable systems. [13][14][15][16][17] Bilinear systems are a class of simple nonlinear systems, and can naturally describe many objects in the process of industrial production, especially some objects in the chemical process, such as the heat exchanger and the synthetic ammonia reactor. 18 Bilinear systems can be viewed as a bridge between linear and nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Iterative identification methods for a class of bilinear systems by using the particle filtering technique

2021

Adaptive Control & Signal

117

View full text Add to dashboard Cite

This article mainly studies the iterative parameter estimation problems of a class of nonlinear systems. Based on the auxiliary model identification idea, this article utilizes the estimated parameters to construct an auxiliary model, and uses its outputs to replace the unknown noise-free process outputs, and develops an auxiliary model least squares-based iterative (AM-LSI) identification algorithm.For further improving the parameter estimation accuracy, we use a particle filter to estimate the unknown noise-free process outputs, and derive a particle filtering least squares-based iterative (PF-LSI) identification algorithm. During each iteration, the AM-LSI and PF-LSI algorithms can make full use of the measured input-output data. The simulation results indicate that the proposed algorithms are effective for identifying the nonlinear systems, and can generate more accurate parameter estimates than the auxiliary model-based recursive least squares algorithm.

show abstract

State filtering and parameter estimation for two‐input two‐output systems with time delay

Cited by 11 publications

References 70 publications

Multi‐innovation Newton recursive methods for solving the support vector machine regression problems

Multi‐innovation Newton recursive methods for solving the support vector machine regression problems

Distributed joint parameter and state estimation algorithm for large‐scale interconnected systems

Iterative identification methods for a class of bilinear systems by using the particle filtering technique

Contact Info

Product

Resources

About