scholarly journals Support Vector Regression for the Modeling and Synthesis of Near-Field Focused Antenna Arrays

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1352 ◽  
Author(s):  
Rafael González Ayestarán
Keyword(s):  
Support Vector Regression ◽  
Antenna Arrays ◽  
Near Field ◽  
Computational Cost ◽  
Support Vector ◽  
Analysis Tool ◽  
Training Samples ◽  
Learning Capabilities ◽  
Novel Method ◽  
The Individual

The powerful support vector regression framework is proposed in a novel method for near-field focusing using antenna arrays. By using this machine-learning method, the set of weights required in the elements of an array can be calculated to achieve an assigned near-field distribution focused on one or more positions. The computational cost is concentrated in an initial training process so that the trained system is fast enough for applications where moving devices are involved. The increased learning capabilities of support vector machines allow using a reduced number of training samples. Thus, these training samples may be generated with a prototype or a convenient electromagnetic analysis tool, and hence realistic effects, such as coupling or the individual radiation patterns of the elements of the arrays, are accounted for. Illustrative examples are presented.

Research on Patch Near-Field Acoustic Holography

2011 ◽  
Vol 105-107 ◽  
pp. 196-199 ◽  
Author(s):  
Hai Chao Zhu ◽  
Zhi Min Chen ◽  
Xiang Hua Du ◽  
Rong Fu Mao
Keyword(s):  
Support Vector Regression ◽  
Sound Pressure ◽  
Near Field ◽  
Measured Data ◽  
Experimental Results ◽  
Support Vector ◽  
Reconstruction Accuracy ◽  
Acoustic Holography ◽  
Training Samples ◽  
Regression Functions

Support vector regression is used to establish a kind of patch near-field acoustic holography. The regression functions are constructed by treating the measured data on the patch holography as training samples, and then the data outside the measurement aperture are extrapolated. The experimental results show that the extrapolation of the sound pressure outside the smaller initial hologram aperture may be realized easily and effectively, and the reconstruction accuracy is satisfactory.

scholarly journals Development of a Hierarchical Support Vector Regression-Based In Silico Model for Caco-2 Permeability

Pharmaceutics ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 174 ◽  
Author(s):  
Giang Huong Ta ◽  
Cin-Syong Jhang ◽  
Ching-Feng Weng ◽  
Max K. Leong
Keyword(s):  
Drug Discovery ◽  
Support Vector Regression ◽  
Human Colon ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Human Colon Carcinoma Cell ◽  
Drug Discovery And Development ◽  
Training Samples ◽  
Experimental Values

Drug absorption is one of the critical factors that should be taken into account in the process of drug discovery and development. The human colon carcinoma cell layer (Caco-2) model has been frequently used as a surrogate to preliminarily investigate the intestinal absorption. In this study, a quantitative structure–activity relationship (QSAR) model was generated using the innovative machine learning-based hierarchical support vector regression (HSVR) scheme to depict the exceedingly confounding passive diffusion and transporter-mediated active transport. The HSVR model displayed good agreement with the experimental values of the training samples, test samples, and outlier samples. The predictivity of HSVR was further validated by a mock test and verified by various stringent statistical criteria. Consequently, this HSVR model can be employed to forecast the Caco-2 permeability to assist drug discovery and development.

scholarly journals A novel method for total chlorine detection using machine learning with electrode arrays

RSC Advances ◽  
2019 ◽  
Vol 9 (59) ◽  
pp. 34196-34206
Author(s):  
Zhe Li ◽  
Shunhao Huang ◽  
Juan Chen
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cyclic Voltammetry ◽  
Support Vector Regression ◽  
Principal Component ◽  
Measurement Model ◽  
Support Vector ◽  
Electrode Arrays ◽  
Soft Measurement ◽  
Novel Method

Establish soft measurement model of total chlorine: cyclic voltammetry curves, principal component analysis and support vector regression.

scholarly journals Near-field sound source localization using principal component analysis–multi-output support vector regression

2020 ◽  
Vol 16 (4) ◽  
pp. 155014772091640
Author(s):  
Lanmei Wang ◽  
Yao Wang ◽  
Guibao Wang ◽  
Jianke Jia
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Regression ◽  
Covariance Matrix ◽  
Signal To Noise Ratio ◽  
Near Field ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Signal To Noise ◽  
Noise Ratio

In this article, principal component analysis method, which is applied to image compression and feature extraction, is introduced into the dimension reduction of input characteristic variable of support vector regression, and a method of joint estimation of near-field angle and range based on principal component analysis dimension reduction is proposed. Signal-to-noise ratio and calculation amount are the decisive factors affecting the performance of the algorithm. Principal component analysis is used to fuse the main characteristics of training data and discard redundant information, the signal-to-noise ratio is improved, and the calculation amount is reduced accordingly. Similarly, support vector regression is used to model the signal, and the upper triangular elements of the signal covariance matrix are usually used as input features. Since the covariance matrix has more upper triangular elements, training it as a feature input will affect the training speed to some extent. Principal component analysis is used to reduce the dimensionality of the upper triangular element of the covariance matrix of the known signal, and it is used as the input feature of the multi-output support vector regression machine to construct the near-field parameter estimation model, and the parameter estimation of unknown signal is herein obtained. Simulation results show that this method has high estimation accuracy and training speed, and has strong adaptability at low signal-to-noise ratio, and the performance is better than that of the back-propagation neural network algorithm and the two-step multiple signal classification algorithm.

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.

Wine Evaluation Modeling Based on Lasso and Support Vector Regression

2017 ◽  
Vol 21 (6) ◽  
pp. 998-1008
Author(s):  
Yanyun Yao ◽  
Bing Xu ◽  
Jinghui He ◽  
◽  
Keyword(s):  
Support Vector Regression ◽  
Nonlinear Effect ◽  
Evaluation System ◽  
Evaluation Model ◽  
Digital Simulation ◽  
Objective Evaluation ◽  
Support Vector ◽  
Lasso Regression ◽  
Wine Quality ◽  
Training Samples

Wine consumption is gaining popularity, and significant attention has been given to its quality. In the present paper, an objective evaluation model along with a reliability test via Lasso and nonlinear effect test via support vector regression (SVR) is proposed. The digital simulation is finished with the experimental data obtained from the A problem of CUMCM-2012 (China Undergraduate Mathematical Contest in Modeling in 2012). The results of Lasso regression show that the wine quality mainly depends upon eight physicochemical indicators. Further research results of SVR imply that with several training samples, a good evaluation can be realized, denoting that our model based on Lasso SVR can significantly reduce the costs of measurement and appraisal. Compared to other relevant articles, this paper builds an objective and credible wine evaluation system where the physicochemical indicators and the latent nonlinear effect are considered. Moreover, the evaluation costs are taken into account.

A novel method based on least squares support vector regression combing with strong tracking particle filter for machinery condition prognosis

2013 ◽  
Vol 228 (6) ◽  
pp. 1048-1062 ◽  
Author(s):  
Chengliang Li ◽  
Zhongsheng Wang ◽  
Shuhui Bu ◽  
Hongkai Jiang ◽  
Zhenbao Liu
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Least Squares ◽  
Support Vector Regression ◽  
Support Vector ◽  
Extend Kalman Filter ◽  
Combining Data ◽  
Novel Method ◽  
Strong Tracking Filter ◽  
Tracking Filter

A reliable prediction method is very important to avoid a catastrophic failure. This paper presents a novel method for machinery condition prognosis, named least squares support vector regression strong tracking particle filter which is based on least squares support vector regression combing with strong tracking particle filter. There are two main contributions in our work: first, the regression function of least squares support vector regression is extended, which constructs a bridge for the application of combining data-driven method with a recursive filter based on extend Kalman filter; second, an extend Kalman filter-based particle filter is studied by introducing a strong tracking filter into a particle filter. The strong tracking filter is used to update particles and produce importance densities which can improve the performance of the particle filter in tracking saltatory states, and finally strong tracking particle filter improves the prediction performance of least squares support vector regression in predicting saltatory states. In the experiment, it can be concluded that the proposed method is better than classical condition predictors in machinery condition prognosis.

A bisection-sampling-based support vector regression–high-dimensional model representation metamodeling technique for high-dimensional problems

2016 ◽  
Vol 231 (12) ◽  
pp. 2173-2186 ◽  
Author(s):  
Yaping Ju ◽  
Geoff Parks ◽  
Chuhua Zhang
Keyword(s):  
Support Vector Regression ◽  
Computational Cost ◽  
Model Representation ◽  
High Dimensional ◽  
Support Vector ◽  
High Dimensional Model Representation ◽  
Dimensional Model ◽  
Comparison Results ◽  
Modeling Accuracy ◽  
Metamodeling Technique

A major challenge of metamodeling in simulation-based engineering design optimization is to handle the “curse of dimensionality,” i.e. the exponential growth of computational cost with increase of problem dimensionality. Encouragingly, it has been reported recently that a high-dimensional model representation assisted by a radial basis function is capable of deriving high-dimensional input–output relationships at dramatically reduced computational cost. In this article, support vector regression is employed as an alternative to be coupled with high-dimensional model representation for the metamodeling of high-dimensional problems. In particular, the bisection sampling method is proposed to be used in the metamodeling process to generate high-quality training samples. Testing and comparison results show that the developed bisection-sampling-based support vector regression–high-dimensional model representation metamodeling technique can achieve high modeling accuracy with a smaller number of training sample evaluations. For the problem examined in this study, the bisection-sampling-based support vector regression–high-dimensional model representation enables high modeling accuracy and linear computational complexity as the problem dimensionality increases. Analysis of this performance advantage shows that the use of bisection method enables the developed metamodeling technique to be more effective in dealing with high-dimensional problems.

Sign in / Sign up

Export Citation Format

Share Document