Wavelet transform-based weighted $$\nu$$-twin support vector regression

2019 ◽  
Vol 11 (1) ◽  
pp. 95-110 ◽  
Author(s):  
Lidong Wang ◽  
Chuang Gao ◽  
Nannan Zhao ◽  
Xuebo Chen
Keyword(s):  
Wavelet Transform ◽  
Support Vector Regression ◽  
Support Vector ◽  
Twin Support Vector Regression

Robust and sparse Dual Tree Complex Wavelet Transform-based Twin Support Vector regression for dense 5G InH  Communications

2021 ◽  
Author(s):  
anis charrada ◽  
Abdelaziz Samet
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Support Vector Regression ◽  
Path Loss ◽  
Series Data ◽  
Support Vector ◽  
Sensitivity Threshold ◽  
Discrete Wavelet ◽  
Twin Support Vector Regression ◽  
Receiver Sensitivity

Abstract A robust and sparse Twin Support Vector Regression based on Dual Tree Discrete Wavelet Transform algorithm is conceived in this paper and applied to 28, 38, 60 and 73-GHz LOS (Line-of-Sight) wireless multipath transmission system in 5G Indoor Hotspot (InH) settings (simple, semi-complex and complex conference rooms) under small receiver sensitivity threshold. The algorithm establishes a denoising process in the learning phase based on Dual Tree Discrete Wavelet Transform (DT-CWT) which is suitable for time-series data. Additionally, the Close-In (CI) free space reference distance path loss model is analyzed and the large-scale propagation and probability distribution functions are investigated by determining the PLE (Path Loss Exponent) and the standard deviation of Shadow Factor (SF) for each InH scenario under consideration. Performance are evaluated under twelve (12) configuration scenarios, according to three criteria: mobility (0/3mps), receiver sensitivity threshold (-80/-120 dBm) and type of the InH area (simple, semi-complex and complex conference room). Experimental results confirm the effectiveness of the proposed approach compared to other standard techniques.

scholarly journals End-Point Static Control of Basic Oxygen Furnace (BOF) Steelmaking Based on Wavelet Transform Weighted Twin Support Vector Regression

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Chuang Gao ◽  
Minggang Shen ◽  
Xiaoping Liu ◽  
Lidong Wang ◽  
Maoxiang Chu
Keyword(s):  
Wavelet Transform ◽  
Support Vector Regression ◽  
Error Bound ◽  
Performance Test ◽  
Prediction Models ◽  
Basic Oxygen Furnace ◽  
Control Model ◽  
Support Vector ◽  
Twin Support Vector Regression ◽  
Hit Rate

A static control model is proposed based on wavelet transform weighted twin support vector regression (WTWTSVR). Firstly, new weighted matrix and coefficient vector are added into the objective functions of twin support vector regression (TSVR) to improve the performance of the algorithm. The performance test confirms the effectiveness of WTWTSVR. Secondly, the static control model is established based on WTWTSVR and 220 samples in real plant, which consists of prediction models, control models, regulating units, controller, and BOF. Finally, the results of proposed prediction models show that the prediction error bound with 0.005% in carbon content and 10°C in temperature can achieve a hit rate of 92% and 96%, respectively. In addition, the double hit rate of 90% is the best result by comparing with four existing methods. The results of the proposed static control model indicate that the control error bound with 800 Nm3 in the oxygen blowing volume and 5.5 tons in the weight of auxiliary materials can achieve a hit rate of 90% and 88%, respectively. Therefore, the proposed model can provide a significant reference for real BOF applications, and also it can be extended to the prediction and control of other industry applications.

An adaptive twin support vector regression machine based on rough and fuzzy set theories

2018 ◽  
Vol 32 (9) ◽  
pp. 4709-4732
Author(s):  
Zhenxia Xue ◽  
Roxin Zhang ◽  
Chuandong Qin ◽  
Xiaoqing Zeng
Keyword(s):  
Support Vector Regression ◽  
Fuzzy Set ◽  
Support Vector ◽  
Twin Support Vector Regression

The Research of Fuzzy Weighted Twin Support Vector Regression in the MBR Simulation Prediction

2014 ◽  
Vol 556-562 ◽  
pp. 3648-3653 ◽  
Author(s):  
Chan Juan Ji ◽  
Chun Qing Li ◽  
Tao Wang
Keyword(s):  
Support Vector Regression ◽  
Membrane Fouling ◽  
Principal Component ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Negative Effects ◽  
Twin Support Vector Regression ◽  
Output Layer ◽  
Input Layer

This paper using the way of Support Vector Data Description (SVDD) and considering the tightness between the Membrane Bio-Reactor (MBR) samples, applies the Fuzzy Weighted Twin Support Vector Regression (FTSVR) to the MBR simulation prediction research. Firstly,adopt the principal component analysis (PCA) on membrane fouling factors to achieve dimension reduction and de-correlation, then put the PCA output layer as the input layer of FTSVR, flux as the output layer, eventually, the MBR Membrane Fouling Prediction Model is built. This method considers the different effects on the regression hyperplane of different MBR samples,and effectively eliminates the negative effects due to error even outliers in the process of MBR data measurement.

A weighted twin support vector regression

2012 ◽  
Vol 33 ◽  
pp. 92-101 ◽  
Author(s):  
Yitian Xu ◽  
Laisheng Wang
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Twin Support Vector Regression
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