Support Vector Regression Modeling for Design and Analysis of Mechanical Snubbing

2010 ◽  
Author(s):  
Sudhir Kaul
Keyword(s):  
Theoretical Model ◽  
Support Vector Regression ◽  
Dynamic Systems ◽  
Hysteretic Behavior ◽  
Model Complexity ◽  
Computational Time ◽  
Support Vector ◽  
Isolation System ◽  
Single Degree Of Freedom ◽  
Design Cycle

This paper discusses the application of Support Vector Regression (SVR) for modeling the non-linear and hysteretic behavior exhibited by mechanical snubbing systems. Though the discussion in this paper is limited to the application of SVR to snubbing in elastomeric isolators, the approach is generic and can be applied to other dynamic systems or to systems exhibiting hysteretic behavior. A theoretical model that represents the coupled dynamics of an isolation system with the corresponding snubbing system for a single degree-of-freedom system is proposed. The theoretical model is experimentally validated and is subsequently used to build a metamodel using SVR. The results of the metamodel are compared to the theoretical model for a simulation example and are found to be comparable, thereby reducing the computational time for the design and analysis of the snubbing system by orders of magnitude. The SVR based metamodel can, therefore, be used to substitute the computationally intense theoretical model for performing design iterations and design optimization of the snubbing system, significantly reducing model complexity as well as computational time during the design cycle.

An adaptive SVR-HDMR model for approximating high dimensional problems

2015 ◽  
Vol 32 (3) ◽  
pp. 643-667 ◽  
Author(s):  
Zhiyuan Huang ◽  
Haobo Qiu ◽  
Ming Zhao ◽  
Xiwen Cai ◽  
Liang Gao
Keyword(s):  
Support Vector Regression ◽  
Direct Method ◽  
Model Representation ◽  
Computational Time ◽  
High Dimensional ◽  
Support Vector ◽  
High Dimensional Model Representation ◽  
Model Assessment ◽  
Dimensional Model ◽  
Content Type

Purpose – Popular regression methodologies are inapplicable to obtain accurate metamodels for high dimensional practical problems since the computational time increases exponentially as the number of dimensions rises. The purpose of this paper is to use support vector regression with high dimensional model representation (SVR-HDMR) model to obtain accurate metamodels for high dimensional problems with a few sampling points. Design/methodology/approach – High-dimensional model representation (HDMR) is a general set of quantitative model assessment and analysis tools for improving the efficiency of deducing high dimensional input-output system behavior. Support vector regression (SVR) method can approximate the underlying functions with a small subset of sample points. Dividing Rectangles (DIRECT) algorithm is a deterministic sampling method. Findings – This paper proposes a new form of HDMR by integrating the SVR, termed as SVR-HDMR. And an intelligent sampling strategy, namely, DIRECT method, is adopted to improve the efficiency of SVR-HDMR. Originality/value – Compared to other metamodeling techniques, the accuracy and efficiency of SVR-HDMR were significantly improved. The SVR-HDMR helped engineers understand the essence of underlying problems visually.

scholarly journals Fuzzy Weighted Least Squares Support Vector Regression with Data Reduction for Nonlinear System Modeling

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaoyong Liu ◽  
Aijia Ouyang ◽  
Zhonghua Yun
Keyword(s):  
Nonlinear System ◽  
Least Squares ◽  
Support Vector Regression ◽  
Data Reduction ◽  
System Modeling ◽  
Weighted Least Squares ◽  
Computational Time ◽  
Support Vector ◽  
Nonlinear System Modeling ◽  
Overlap Factor

This paper proposes a fuzzy weighted least squares support vector regression (FW-LSSVR) with data reduction for nonlinear system modeling based only on the measured data. The proposed method combines the advantages of data reduction with some ideas of fuzzy weighted mechanism. It not only possesses the capability of illuminating local characteristic of the modeled plant but also can deal with the problem of boundary effects resulted from local LSSVR method when the modeled data is at the boundary of whole data subset. Furthermore, in comparison of the SVR, the proposed method only utilizes fewer hyperparameters to construct model, and the overlap factor λ can be chosen in relatively smaller value than SVR to further reduce more computational time. First of all, distilling the original input space into several regions with fuzzy partition by applying Gustafson-Kessel clustering algorithm (GKCA) is a foundation for data reduction and the overlap factor is introduced to reduce the size of subsets. Following that, those subset regression models (SRMs) which can be simultaneously solved by LSSVR are integrated into an overall output of the estimated nonlinear system by fuzzy weighted. Finally, the proposed method is demonstrated by experimental analysis and compared with local LSSVR, weighted SVR, and global LSSVR methods by using the index of computational time and root-mean-square error (RMSE).

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection

Data Analytics to Help Improve Hydraulic Fracture Geometry Prediction Using Support Vector Regression: Case Study

2020 ◽  
Author(s):  
Avinash Wesley ◽  
Bharat Mantha ◽  
Ajay Rajeev ◽  
Aimee Taylor ◽  
Mohit Dholi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Hydraulic Fracture ◽  
Data Analytics ◽  
Support Vector ◽  
Fracture Geometry

IMPLEMENTASI SUPPORT VECTOR MACHINE PADA PREDIKSI HARGA SAHAM GABUNGAN (IHSG)

2020 ◽  
Vol 25 (1) ◽  
pp. 24-38
Author(s):  
Eka Patriya
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Regression ◽  
Support Vector

Saham adalah instrumen pasar keuangan yang banyak dipilih oleh investor sebagai alternatif sumber keuangan, akan tetapi saham yang diperjual belikan di pasar keuangan sering mengalami fluktuasi harga (naik dan turun) yang tinggi. Para investor berpeluang tidak hanya mendapat keuntungan, tetapi juga dapat mengalami kerugian di masa mendatang. Salah satu indikator yang perlu diperhatikan oleh investor dalam berinvestasi saham adalah pergerakan Indeks Harga Saham Gabungan (IHSG). Tindakan dalam menganalisa IHSG merupakan hal yang penting dilakukan oleh investor dengan tujuan untuk menemukan suatu trend atau pola yang mungkin berulang dari pergerakan harga saham masa lalu, sehingga dapat digunakan untuk memprediksi pergerakan harga saham di masa mendatang. Salah satu metode yang dapat digunakan untuk memprediksi pergerakan harga saham secara akurat adalah machine learning. Pada penelitian ini dibuat sebuah model prediksi harga penutupan IHSG menggunakan algoritma Support Vector Regression (SVR) yang menghasilkan kemampuan prediksi dan generalisasi yang baik dengan nilai RMSE training dan testing sebesar 14.334 dan 20.281, serta MAPE training dan testing sebesar 0.211% dan 0.251%. Hasil penelitian ini diharapkan dapat membantu para investor dalam mengambil keputusan untuk menyusun strategi investasi saham.

Support Vector Regression Optimization Problem without Bias

2012 ◽  
Vol 23 (9) ◽  
pp. 2336-2346
Author(s):  
Xiao-Jian DING ◽  
Yin-Liang ZHAO
Keyword(s):  
Support Vector Regression ◽  
Optimization Problem ◽  
Support Vector
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