Unstable unsteady aerodynamic modeling based on least squares support vector machines with general excitation

Chen, Senlin; Gao, Zhenghong; Zhu, Xinqi; Du, Yajie; Pang, Chengzong

doi:10.1016/j.cja.2020.03.009

Cited by 21 publications

(6 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SVM has been widely used in the field of classification and regression [22], [23]. Similar to ANN, SVR is able to realize nonlinear mapping from input space to output space, and shows a superior generalization performance over ANN especially for small size sample [24].…”

Section: Limitations and Contributionsmentioning

confidence: 99%

SVR-ND Method for Online Aerodynamic Parameter Estimation

Wei

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Section: Limitations and Contributionsmentioning

confidence: 99%

SVR-ND Method for Online Aerodynamic Parameter Estimation

Wei

et al. 2020

IEEE Access

View full text Add to dashboard Cite

“…With a sufficient amount of training data, these models possess powerful nonlinear fitting capabilities to establish the relationship between flight states and aerodynamic loads. Models such as support vector machines [7,8], random forest [9], and neural networks [10][11][12] have been employed for this purpose. Furthermore, recurrent neural networks improve accuracy by capturing the time lag effects of unsteady aerodynamics [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A Weighted Feature Fusion Model for Unsteady Aerodynamic Modeling at High Angles of Attack

Dong,

Wang,

Lin

et al. 2024

Aerospace

View full text Add to dashboard Cite

Unsteady aerodynamic prediction at high angles of attack is of great importance to the design and development of advanced fighters. In this paper, a weighted feature fusion model (WFFM) that combines the state-space model and neural networks is proposed to build an unsteady aerodynamic model for the precise simulation and control of post-stall maneuvers. In the proposed model, the influences of the physical model on neural networks are considered and adjusted by introducing a standardization layer and a new weighting method. A long short-term memory (LSTM) network is used to fuse two mappings: one from flight states to aerodynamic loads, and the other from low-fidelity data to high-fidelity data. Data from wind tunnel oscillation experiments at high angles of attack using a new kind of wire-driven parallel robot and the traditional tail support are used for verifying the proposed aerodynamic model. The output of the WFFM is also compared with predictions from other models, such as the state-space model, single LSTM model, and feature fusion model not including a feature weighting layer. Results demonstrate improved accuracy of the proposed model in the interpolation and extrapolation tests. Furthermore, the WFFM is applied to the flight simulation of F-16 with different control inputs. Compared with conventional models, the WFFM shows improved accuracy and better generalization capability.

show abstract

“…Among the machine learning methods, SVM, with powerful generalization ability and high learning efficiency, has been widely used as a fault detection model for bearings [ 24 , 25 , 26 ], motors [ 27 ], gearboxes [ 28 , 29 ], and pumps [ 30 ]. Improved algorithms for SVM include the wavelet transform-based SVM [ 31 ], least squares-based SVM [ 32 , 33 ], hyper-sphere-structured SVM [ 34 ], proximal SVM [ 35 ], etc. In addition, feature selection in learning has recently emerged as a crucial issue [ 36 , 37 ].…”

Section: Introductionmentioning

confidence: 99%

Piston Wear Detection and Feature Selection Based on Vibration Signals Using the Improved Spare Support Vector Machine for Axial Piston Pumps

Xia

Xiang

2022

Materials

View full text Add to dashboard Cite

A piston wear fault is a major failure mode of axial piston pumps, which may decrease their volumetric efficiency and service life. Although fault detection based on machine learning theory can achieve high accuracy, the performance mainly depends on the detection model and feature selection. Feature selection in learning has recently emerged as a crucial issue. Therefore, piston wear detection and feature selection are essential and urgent. In this paper, we propose a vibration signal-based methodology using the improved spare support vector machine, which can integrate the feature selection into the piston wear detection learning process. Forty features are defined to capture the piston wear signature in the time domain, frequency domain, and time–frequency domain. The relevance and impact of sparsity in 40 features are illustrated through the single and multiple statistical feature analysis. Model performance is assessed and the sparse features are discovered. The maximum model testing and training accuracy are 97.50% and 96.60%, respectively. Spare features s10, s12, Ew(8), x7, Ee(5), and Ee(4) are selected and validated. Results show that the proposed methodology is applicable for piston wear detection and feature selection, with high model accuracy and good feature sparsity.

show abstract

Unstable unsteady aerodynamic modeling based on least squares support vector machines with general excitation

Cited by 21 publications

References 21 publications

SVR-ND Method for Online Aerodynamic Parameter Estimation

SVR-ND Method for Online Aerodynamic Parameter Estimation

A Weighted Feature Fusion Model for Unsteady Aerodynamic Modeling at High Angles of Attack

Piston Wear Detection and Feature Selection Based on Vibration Signals Using the Improved Spare Support Vector Machine for Axial Piston Pumps

Contact Info

Product

Resources

About