Parameter Estimation of Wiener Systems Based on the Particle Swarm Iteration and Gradient Search Principle

Li, Junhong; Zong, Tiancheng; Gu, Juping; Hua, Liu

doi:10.1007/s00034-019-01329-1

Cited by 41 publications

(21 citation statements)

References 34 publications

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“…Therefore, system identification, a kind of experimental identification method, becomes the common choice for establishing the mathematical models of linear or nonlinear systems. 29,30 Different identification methods have been developed for Wiener systems, 31 Hammerstein systems [32][33][34] and other nonlinear systems. 35,36 In the fields of the identification of bilinear systems, many identification methods have been derived for bilinear systems with different description forms.…”

Section: Introductionmentioning

confidence: 99%

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

2021

Adaptive Control & Signal

134

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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.

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Section: Introductionmentioning

confidence: 99%

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

2021

Adaptive Control & Signal

134

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“…System identification is theory of establishing the mathematical models of dynamical systems by measuring the system inputs and outputs 1‐3 . System modeling and parameter estimation are the basis of all the control problems, 4‐9 so it is significant to build an appropriate model for system prediction and control 10‐15 . Many identification methods have been developed for linear systems, but most practical systems have nonlinear characteristics in nature.…”

Section: Introductionmentioning

confidence: 99%

Identification of the nonlinear systems based on the kernel functions

Ding

Yang

2021

Intl J Robust & Nonlinear

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Constructing an appropriate membership function is significant in fuzzy logic control. Based on the multi‐model control theory, this article constructs a novel kernel function which can implement the fuzzification and defuzzification processes and reflect the dynamic quality of the nonlinear systems accurately. Then we focus on the identification problems of the nonlinear systems based on the kernel functions. Applying the hierarchical identification principle, we present the hierarchical stochastic gradient algorithm for the nonlinear systems. Meanwhile, the one‐dimensional search methods are proposed to solve the problem of determining the optimal step sizes. In order to improve the parameter estimation accuracy, we propose the hierarchical multi‐innovation forgetting factor stochastic gradient algorithm by introducing the forgetting factor and using the multi‐innovation identification theory. The simulation example is provided to test the proposed algorithms from the aspects of parameter estimation accuracy and prediction performance.

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“…Various models are exploited for different kinds of non-linearities [13][14][15]. Typical non-linear models include Volterra models, Hammerstein models and Wiener models [16][17][18]. The Hammerstein model, which is composed of a static nonlinear block followed by a dynamic linear block [19,20], has been applied to many fields, such as chemical processes, fuel cells, battery and biological processes.…”

Section: Introductionmentioning

confidence: 99%

The robust multi‐innovation estimation algorithm for Hammerstein non‐linear systems with non‐Gaussian noise

Wang

Ding

2021

IET Control Theory & Appl

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The characteristic of the external noise has significant influences on system modelling and identification, and the assumption that the noise follows the Gaussian distribution may be invalid due to realistic reasons. This paper discusses the identification issue of Hammerstein non-linear systems with non-Gaussian noise and presents a robust gradient algorithm. The algorithm is derived based on the logarithmic cost function of continuous mixed pnorm of prediction errors, which takes into account each p-norm of errors for 1 ⩽ p ⩽ 2. The gain at each recursive step adapts to the data quality so that the algorithm has good robustness to non-Gaussian noise. To improve the estimation accuracy, a robust multiinnovation gradient algorithm is proposed by using the multi-innovation identification theory. Two examples are provided to exhibit the validity of the proposed algorithms. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Parameter Estimation of Wiener Systems Based on the Particle Swarm Iteration and Gradient Search Principle

Cited by 41 publications

References 34 publications

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

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

Identification of the nonlinear systems based on the kernel functions

The robust multi‐innovation estimation algorithm for Hammerstein non‐linear systems with non‐Gaussian noise

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