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.

Improved Boosting Model for Unsteady Nonlinear Aerodynamics based on Computational Intelligence

2017 ◽  
Vol 11 (1) ◽  
pp. 46-59 ◽  
Author(s):  
Boxu Zhao ◽  
Guiming Luo ◽  
Jihong Zhu
Keyword(s):  
Wind Tunnel ◽  
Polynomial Regression ◽  
Least Square ◽  
Support Vector ◽  
Highly Nonlinear ◽  
Vector Machines ◽  
Large Amplitude Oscillation ◽  
Wind Tunnel Data ◽  
Improved Model ◽  
Oscillation Experiment

The large-amplitude-oscillation experiment was carried out with two levels of freedom to provide data. Based on the wind tunnel data, polynomial regression, least-square support vector machines and radial basis function neural networks are studied and compared in this paper. An improved model was also developed in this work for unsteady nonlinear aerodynamics on the basis of standard boosting approach. The results on the wind tunnel data show that the predictions of the method are almost consistent with the actual data, thus demonstrating that these methods can model highly nonlinear aerodynamics. The results also indicate that improved boosting model has better accuracy than the other methods.

scholarly journals Implementing and evaluating various machine learning models for pipe burst prediction

2021 ◽  
Author(s):  
Ahmad Ravanbakhsh ◽  
Mehdi Momeni ◽  
Amir Robati
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Regression Models ◽  
Distribution Networks ◽  
Least Square ◽  
Multivariate Adaptive Regression Splines ◽  
Fuzzy Regression ◽  
Support Vector ◽  
Hydraulic Pressure ◽  
Pipe Burst

Abstract. By accurate predicting of pipe bursts, it is possible to schedule pipe maintenance, rehabilitation and improve the level of services in water distribution networks (WDNs). In this study, we aimed to implement five artificial intelligence and machine learning regression models such as multivariate adaptive regression splines (MARS), M5' regression tree (M5'), Least square support vector regression (LS-SVR), fuzzy regression based on c-means clustering (FCMR) and regressive convolution neural network with support vector regression (RCNN-SVR) for predicting pipe burst rate and evaluating the performance of these models. The most effective parameters for regression models are pipes age, diameter, depth of installation, length, average and maximum hydraulic pressure. In the present study, collected data include 158 cases for polyethylene (PE) and 124 cases for asbestos cement (AC) pipes during 2012-2019. The results indicate that the RCNN-SVR model has a great performance of pipe burst rate (PBR) prediction.

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.

Ensemble of Meta-Models Based on Local Error Measure Using Cross-Validation

2009 ◽  
Author(s):  
Dohyun Park ◽  
Yongbin Lee ◽  
Dong-Hoon Choi
Keyword(s):  
Prediction Accuracy ◽  
Cross Validation ◽  
Polynomial Regression ◽  
Computational Cost ◽  
Support Vector ◽  
Local Error ◽  
Test Cases ◽  
Meta Model ◽  
Lower Accuracy ◽  
Cross Validation Error

Many meta-models have been developed to approximate true responses. These meta-models are often used for optimization instead of computer simulations which require high computational cost. However, designers do not know which meta-model is the best one in advance because the accuracy of each meta-model becomes different from problem to problem. To address this difficulty, research on the ensemble of meta-models that combines stand-alone meta-models has recently been pursued with the expectation of improving the prediction accuracy. In this study, we propose a selection method of weight factors for the ensemble of meta-models based on v-nearest neighbors’ cross-validation error (CV). The four stand-alone meta-models we employed in this study are polynomial regression, Kriging, radial basis function, and support vector regression. Each method is applied to five 1-D mathematical examples and ten 2-D mathematical examples. The prediction accuracy of each stand-alone meta-model and the existing ensemble of meta-models is compared. Ensemble of meta-models shows higher accuracy than the worst stand-alone model among the four stand-alone meta-models at all test examples (30 cases). In addition, the ensemble of meta-models shows the highest accuracy for the 5 test cases. Although it has lower accuracy than the best stand-alone meta-model, it has almost same RMSE values (less than 1.1) as the best standalone model in 16 out of 30 test cases. From the results, we can conclude that proposed method is effective and robust.

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.

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