Distributed least square support vector regression for environmental field estimation

2011 ◽  
Author(s):  
Bowen Lu ◽  
Dongbing Gu ◽  
Huosheng Hu
Keyword(s):  
Support Vector Regression ◽  
Least Square ◽  
Support Vector ◽  
Field Estimation

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.

scholarly journals Least Square Support Vector Regression-Based Model for Whiteness Index of Cotton Fabric Prediction

2021 ◽  
Author(s):  
Wan Sieng Yeo
Keyword(s):  
Support Vector Regression ◽  
Cotton Fabric ◽  
Predictive Modelling ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Bleaching Process ◽  
Whiteness Index ◽  
Least Square Regression

Abstract The textile bleaching process uses a hydrogen peroxide (H2O2) solution in alkali pH associated with high temperature is the commonly used bleaching procedure in cotton fabric manufacture. The purpose of the bleaching process is to remove the natural colour from cotton to obtain a permanent white colour before dyeing or shape matching. Normally, the visual ratings of whiteness on the cotton are measured by the whiteness index (WI). Notice that lesser research study is focusing on chemical predictive modelling of the WI of cotton fabric than its experimental study. Predictive analytics using predictive modelling can forecast the outcomes that can lead to better-informed cotton quality assurance and control decisions. Up to date, limited study applying least square support vector regression (LSSVR) based model in the textile domain. Hence, the present study was aimed to develop the LSSVR-based model, namely multi-output LSSVR (MLSSVR) using bleaching process variables to predict the WI of cotton. The predictive accuracy of the MLSSVR model is measured by root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2), and its results are compared with other regression models including partial least square regression, predictive fuzzy model, locally weighted partial least square regression and locally weighted kernel partial least square regression. The results indicate that the MLSSVR model performed better than other models in predicting the WI as it has 60–1209% lower values of RMSE and MAE as well as it provided the highest R2 values which are up to 0.9985.

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