scholarly journals Hysteresis and temperature drift compensation for FBG demodulation by utilizing adaptive weight least square support vector regression

2021 ◽  
Author(s):  
WENJUAN SHENG ◽  
HAIQI DANG ◽  
GangDing Peng
Keyword(s):  
Support Vector Regression ◽  
Least Square ◽  
Support Vector ◽  
Temperature Drift ◽  
Adaptive Weight ◽  
Drift Compensation

Integrating Least Square Support Vector Regression and Mode Pursuing Sampling Optimization for Crashworthiness Design

2011 ◽  
Vol 133 (4) ◽  
Author(s):  
Hu Wang ◽  
Songqing Shan ◽  
G. Gary Wang ◽  
Guangyao Li
Keyword(s):  
Support Vector Regression ◽  
Polynomial Regression ◽  
Computational Cost ◽  
Nonlinear Problems ◽  
Integrated Approach ◽  
Least Square ◽  
Multivariate Adaptive Regression Splines ◽  
Support Vector ◽  
Highly Nonlinear ◽  
Crashworthiness Design

Many metamodeling techniques have been developed in the past two decades to reduce the computational cost of design evaluation. With the increasing scale and complexity of engineering problems, popular metamodeling techniques including artificial neural network (ANN), Polynomial regression (PR), Kriging (KG), radial basis functions (RBF), and multivariate adaptive regression splines (MARS) face difficulties in solving highly nonlinear problems, such as the crashworthiness design. Therefore, in this work, we integrate the least support vector regression (LSSVR) with the mode pursuing sampling (MPS) optimization method and applied the integrated approach for crashworthiness design. The MPS is used for generating new samples which are concentrated near the current local minima at each iteration and yet still statistically cover the entire design space. The LSSVR is used for establishing a more robust metamodel from noisy data. Therefore, the proposed method integrates the advantages of both the LSSVR and MPS to more efficiently achieve reasonably accurate results. In order to verify the proposed method, well-known highly nonlinear functions are used for testing. Finally, the proposed method is applied to three typical crashworthiness optimization cases. The results demonstrate the potential capability of this method in the crashworthiness design of vehicles.

scholarly journals Direct Surge Margin Control for Aeroengines Based on Improved SVR Machine and LQR Method

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
Haibo Zhang ◽  
Fengyong Sun
Keyword(s):  
Support Vector Regression ◽  
Stability Margin ◽  
Linear Quadratic Regulator ◽  
Least Square ◽  
Support Vector ◽  
Linear Quadratic ◽  
Inlet Distortion ◽  
Surge Margin ◽  
Engine Stability ◽  
Margin Control

A novel scheme of high stability engine control (HISTEC) on the basis of an improved linear quadratic regulator (ILQR), called direct surge margin control, is derived for super-maneuver flights. Direct surge margin control, which is different from conventional control scheme, puts surge margin into the engine closed-loop system and takes surge margin as controlled variable directly. In this way, direct surge margin control can exploit potential performance of engine more effectively with a decrease of engine stability margin which usually happened in super-maneuver flights. For conquering the difficulty that aeroengine surge margin is undetectable, an approach based on improved support vector regression (SVR) machine is proposed to construct a surge margin prediction model. The surge margin modeling contains two parts: a baseline model under no inlet distortion states and the calculation for surge margin loss under supermaneuvering flight conditions. The previous one is developed using neural network method, the inputs of which are selected by a weighted feature selection algorithm. Considering the hysteresis between pilot input and angle of attack output, an online scrolling window least square support vector regression (LSSVR) method is employed to firstly estimate inlet distortion index and further compute surge margin loss via some empirical look-up tables.

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