Ensemble fixed-size LS-SVMs applied for the Mach number prediction in transonic wind tunnel

Wang, Xiaojun; Ping, Yuan; Mao, Zhizhong

doi:10.1109/taes.2014.130708

Cited by 28 publications

(10 citation statements)

References 21 publications

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“…Projections of the original training data on several subspace subsets are firstly used to learn base neural network models, which are combined then by a fusion rule. In [6,13], algorithm LSSVM is used as base learners and two ensemble Mach number predictive models are proposed. In [8], another ensemble predictive model, in which Gaussian process regression is used as the base learner, is proposed for Mach number prediction in wind tunnel.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, the variance can also be reduced by fusing results of all base models 1 , 2 , … , . Let ̂ be the prediction of model , then the prediction of our ensemble model can be depicted by Formula (6). ̂= (̂1,̂2, … ,̂) (6) where indicates the fusion function used by Bagging.…”

Section: A Robustness Enhancementmentioning

confidence: 99%

“…The sizes of the training and test set are 9000 and 5000, respectively. The order of NARX model is 5 as recommended in [6]. TABLE Ⅱ and Ⅲ present several descriptive statistics of each feature for the training and test dataset, respectively.…”

Section: A Experiments ⅰmentioning

confidence: 99%

“…Accordingly, a parametric predictive model seems appropriate for nonlinear plants. In literature, several machine learning algorithms have been used for forecasting Mach number in wind tunnel, such as neural network (NN) [5], least squares support vector machine (LSSVM) [6], adaptive network-based fuzzy inference system (ANFIS) [7], and Gaussian process regression (GPR) [8]. Note that all these predictive models share the same objective, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing the Prediction of Mach Number in Wind Tunnel With a Regression-Based Outlier Detection Framework

et al. 2021

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Model predictive control (MPC) has been successfully applied in practical wind tunnel systems. As a critical component of MPC, the accurate prediction of Mach number becomes hence very significant. Real measurement data records often contain outliers or noisy data because of the harsh working conditions of practical systems. Such datasets are detrimental to most data-driven Mach number predictors. This paper focuses on improving Mach number prediction via a regression-based outlier detection framework. The development of a regression-based outlier detector for enhancing Mach number prediction is natural since only regression analysis needs to be implemented for both prediction and outlier detection. In contrast to existing regression-based detectors, we have taken into account three problems that may greatly impact the detecting performance. Specifically, we use Bagging technique to reduce the negative influence of unknown outliers during the training phase. Then, the notion of extreme value analysis is used to determine an appropriate threshold for the calculated outlier scores. A robust scaling method based on Hampel identifier is also used to alleviate the influence of outliers on data scaling. Several datasets stemming from a real-world wind tunnel are used in experiments to verify the effectiveness of our detector. The experimental results show that regression-based outlier detectors often perform better if the prior knowledge about the dependent variable is used. In addition, the proposed detector has outperformed other regression-based detectors.

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Section: Related Workmentioning

confidence: 99%

Section: A Robustness Enhancementmentioning

confidence: 99%

Section: A Experiments ⅰmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancing the Prediction of Mach Number in Wind Tunnel With a Regression-Based Outlier Detection Framework

et al. 2021

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“…Inspired by theses problems, a large number of researchers and institutes devote to investigating effective control strategies to guarantee the control performance (see.,e.g. [4], [5] and the reference therein). In the early design of control system for wind tunnel, the classical proportion-integration-differentiation (PID), linear quadratic regulation (LQR) were widely applied.…”

Section: Introductionmentioning

confidence: 97%

MPC-Based Offset-Free Tracking Control for Intermittent Transonic Wind Tunnel

2020

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This paper addresses the offset-free model predictive control (MPC) for the intermittent transonic wind tunnel (ITWT). The offset-free property holds by introducing the controller state equation which introduces an integral action, while the controller state can keep as the decision variable. The infinite-horizon control moves are parameterized as a sequence of degrees of freedom followed by a control law. The terminal constraint set and terminal control law are designed based on the asymptotic invariance ellipsoid in which physical constraints are satisfied. The previous dual-mode MPC is appropriately modified withholding the closed-loop stability. Simulation results verify the effectiveness of the proposed methods.INDEX TERMS Model predictive control, offset-free control, stability, wind tunnel.

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Detecting outliers in industrial systems using a hybrid ensemble scheme

Wang

Mao

2019

Neural Comput & Applic

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Ensemble fixed-size LS-SVMs applied for the Mach number prediction in transonic wind tunnel

Cited by 28 publications

References 21 publications

Enhancing the Prediction of Mach Number in Wind Tunnel With a Regression-Based Outlier Detection Framework

Enhancing the Prediction of Mach Number in Wind Tunnel With a Regression-Based Outlier Detection Framework

MPC-Based Offset-Free Tracking Control for Intermittent Transonic Wind Tunnel

Detecting outliers in industrial systems using a hybrid ensemble scheme

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