Identification of Wiener–Hammerstein models based on variational bayesian approach in the presence of process noise-Reference-Cited by-同舟云学术

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Identification of Wiener–Hammerstein models based on variational bayesian approach in the presence of process noise

Published:2021-07 Issue:10 Volume:358 Page:5623-5638
ISSN:0016-0032
Container-title:Journal of the Franklin Institute
language:en
Short-container-title:Journal of the Franklin Institute

Author:

Liu Qie,Tang Xinming,Li Junhao,Zeng Jianxue,Zhang Ke,Chai Yi

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Ministry of Science and Technology of the People's Republic of China

Publisher

Elsevier BV

Subject

Applied Mathematics,Computer Networks and Communications,Signal Processing,Control and Systems Engineering

Reference30 articles.

1. Modeling and identification of nonlinear systems: areview of the multimodel approach-part 1;Adeniran;IEEE Trans. Syst. Man Cybern.,2016

2. Nonlinear dynamic systems identification using recurrent interval type-2 tsk fuzzy neural network a novel structure;El-Nagar;ISA Trans.,2018

3. Identification of nonlinear Wiener–Hammerstein systems by a novel adaptive algorithm based on cost function framework;Li;ISA Trans.,2018

4. System identification of nonlinear state-space models;Schön;Automatica,2011

5. Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains;Billings,2013

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1. Identification of Wiener–Hammerstein nonlinear systems with backlash operators;International Journal of Dynamics and Control;2024-06-28

2. Parameter estimation of Wiener-Hammerstein system based on multi-population self-adaptive differential evolution algorithm;Engineering Computations;2023-10-03

3. Expectation‐maximization algorithm for bilinear state‐space models with time‐varying delays under non‐Gaussian noise;International Journal of Adaptive Control and Signal Processing;2023-07-12

4. Parameter estimation for nonlinear sandwich system using instantaneous performance principle;PLOS ONE;2022-12-14

5. Identification of fractional order Wiener-Hammerstein systems based on adaptively fuzzy PSO and data filtering technique;Applied Intelligence;2022-10-21

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