Wiener–Hammerstein System Identification: A Fast Approach Through Spearman Correlation

Jiang

IEEE Trans. Neural Syst. Rehabil. Eng.

et al. 2021

The acoustic stimulation influences of the brain is still unveiled, especially from the brain network point, which can reveal how interaction is propagated and integrated between different brain zones for chronic tinnitus patients. We specifically designed a paradigm to record the electroencephalograms (EEGs) for tinnitus patients when they were treated with consecutive acoustic stimulation neuromodulation therapy for up to 75 days, using the tinnitus handicap inventory (THI) to evaluate the tinnitus severity or the acoustic stimulation treatment efficacy, and the EEG to record the brain activities every 2 weeks. Then, we used an EEG-based coherence analysis to investigate if the changes in brain network consistent with the clinical outcomes can be observed during 75-days acoustic treatment. Finally, correlation analysis was conducted to study potential relationships between network properties and tinnitus handicap inventory score change. The EEG network became significantly weaker after long-term periodic acoustic stimulation treatment, and tinnitus handicap inventory score changes or the acoustic stimulation treatment efficacy are strongly correlated with the varying brain network properties. Long-term acoustic stimulation neuromodulation intervention can improve the rehabilitation of chronic tinnitus

Section: ) Statistical Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Correlation Analysis of EEG Brain Network With Modulated Acoustic Stimulation for Chronic Tinnitus Patients

Jiang

IEEE Trans. Neural Syst. Rehabil. Eng.

et al. 2021

“…Numerous identification methods have been proposed to handle the parameter estimation of the sandwich system [6][7][8]. In Shaikh et al [9], a spearman correlation scheme is used to identify the sandwich model, in which the good initial values are obtained by using the best linear approximation approach. Liu [10] proposed an improved bayesian approach to calculate the posterior distribution of the internal variables, used expectation maximization scheme to produce the estimation values of the sandwich system parameters.…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation for nonlinear sandwich system using instantaneous performance principle

Wang

2022

PLoS ONE

The vast majority of reports mainly focus on the steady-state performance of parameter estimation. Few findings are reported for the instantaneous performance of parameter estimation because the instantaneous performance is difficult to quantify by using the design algorithm, for example, in the initial stage of parameter estimation, the error of parameter estimation varies in a specific region on the basis of the user’s request. With that in mind, we design an identification algorithm to address the transient performance of the parameter estimations. In this study, the parameter estimation of nonlinear sandwich system is studied by using the predefined constraint technology and high-effective filter. To achieve the above purpose, the estimation error information reflecting the transient performance of parameter estimation is procured using the developed some intermediate variables. Then, a predefined constraint function is used to prescribe the error convergence boundary, in which the convergence rate is lifted. An error equivalent conversion technique is then employed to obtain the transformed error data for establishing an parameter adaptive update law, in which the estimation error convergence and the predefined domain can be achieved. In comparison with the available estimation schemes, the good instantaneous performance is obtained on the basis of the numerical example and practical process results.

“…Among all the hysteresis models, the Duhem model is a popular hysteresis model because the other hysteresis models can be described by choosing diverse functions of the Duhem model. In this study, as shown in Figure 1, parameter estimation of the Wiener-Hammersteinlike system with Duhem hysteresis model is studied due to the effective description ability for the actual systems such as micropositioning stage [35], crystal detector [36], electromagnetic scanning system [37], and motor servo drive system [21]. Hence, the research of parameter identification for Wiener-Hammerstein-like system with Duhem hysteresis has practical significance.…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification with a New Recursive Framework for Wiener–Hammerstein-Like System and Its Application

Liang

Mathematical Problems in Engineering

et al. 2022

In the process of actual system modeling, many systems exhibit nonlinear characteristics with memory. Thus, the parameter identification problem of the nonlinear system with memory usually appears in the system modeling. This report focuses on the nonlinear system identification of Wiener–Hammerstein-like model with memory hysteresis, in which a new recursive estimation way is introduced. In this algorithm, the estimation bias problem can be improved by introducing a data filtering technique. On the basis of the filtered data, some auxiliary matrices and vectors are proposed. Following this, the identification error variable is introduced by using auxiliary matrices and vectors with an adaptive forgetting factor. Afterward, the identification error variable is integrated into the design of parameter estimation adaptive law with recursive gain structure. By comparison with the classic estimation methods, the proposed algorithm shows an alternative identification algorithm design angle. In addition, it is strictly proved that the parameter estimation error converges to zero under a general excitation condition. Based on the results of indices M S E , compared with the existing methods, the performance improvements of the proposed method are 33.9 %, 41.26%, and 53.5%, respectively. In terms of indices P E M , the augmented performances of the developed scheme are 50%, 56.2%, and 68.4%, respectively, in comparison to the available schemes.