Fault Diagnosis of Train Plug Door Based on a Hybrid Criterion for IMFs Selection and Fractional Wavelet Package Energy Entropy

Intl J Robust & Nonlinear

2019

106

Summary This paper is concerned with the joint estimation of states and parameters of a special class of nonlinear systems, ie, bilinear systems. The key is to investigate new estimation methods for interactive state and parameter estimation of the considered system based on the interactive estimation theory. Because the system states are unknown, a bilinear state observer is established based on the Kalman filtering principle. Then, the unavailable states are updated by the state observer outputs recursively. Once the state estimates are obtained, the bilinear state observer–based hierarchical stochastic gradient algorithm is developed by using the gradient search. For the purpose of improving the convergence rate and the parameter estimation accuracy, a bilinear state observer–based hierarchical multi‐innovation stochastic gradient algorithm is proposed by expanding a scalar innovation to an innovation vector. The convergence analysis indicates that the parameter estimates can converge to their true values. The numerical example illustrates the effectiveness of the proposed algorithms.

Section: Discussionmentioning

confidence: 99%

Recursive parameter estimation methods and convergence analysis for a special class of nonlinear systems

Xiao

Intl J Robust & Nonlinear

2019

106

“…The simulation results indicate that the proposed algorithms are effective for parameter estimation and that the ML-ADE algorithm is capable of improving the parameter estimation accuracy of the maximum likelihood-based differential evolution algorithm. The proposed maximum likelihood-based adaptive differential evolution algorithm in this article can be applied to other control and schedule areas such as transportation communication systems, [65][66][67][68] engineering application systems, [69][70][71][72] and so on. System identification includes two main aspects: model structure identification and parameter estimation.…”

Section: Discussionmentioning

confidence: 99%

Maximum likelihood‐based adaptive differential evolution identification algorithm for multivariable systems in the state‐space form

Cui

Adaptive Control & Signal

et al. 2020

Parameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state-space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood-based differential evolution (ML-DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML-DE algorithm and propose a maximum likelihood-based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms.

“…Compared with the F-GSG algorithm, the F-MI-GSG, and F-MI-GFG algorithms based on the data filtering technique and multiinnovation identification theory have higher parameter estimation accuracy. The proposed parameter identification algorithms for a nonlinear RBF-ARAR network in this article can be extended to other bilinear and nonlinear systems and can be applied to other areas [70][71][72][73][74] such as the information processing and transportation communication systems [75][76][77][78][79][80][81] and so on.…”

Section: Discussionmentioning

confidence: 99%

Parameter identification of a nonlinear radial basis function‐based state‐dependent autoregressive network with autoregressive noise via the filtering technique and the multiinnovation theory

Zhou

Intl J Robust & Nonlinear

et al. 2020

This article studies the parameter estimation problems of radial basis function-based state-dependent autoregressive models with autoregressive noises (RBF-ARAR models). To reduce the effect of the colored noise to parameter estimation, the data filtering technique is applied and a filtering based generalized stochastic gradient algorithm is derived for the RBF-ARAR models. In order to achieve more accurate parameter estimates, a filtering based multiinnovation generalized stochastic gradient (F-MI-GSG) algorithm is proposed by utilizing the current and past innovations. Introducing two forgetting factors, a filtering based multiinnovation generalized forgetting gradient algorithm is developed to improve the transient performance of the F-MI-GSG algorithm. The effectiveness of the proposed algorithms is verified through the simulation examples.