scholarly journals Improving the estimation accuracy of titration-based asphaltene precipitation through power-law committee machine (PLCM) model with alternating conditional expectation (ACE) and support vector regression (SVR) elements

2015 ◽  
Vol 6 (2) ◽  
pp. 265-277 ◽  
Author(s):  
Amin Gholami ◽  
Omid Mohammadzadeh ◽  
Shahin Kord ◽  
Siyamak Moradi ◽  
Bahram Dabir
Keyword(s):  
Support Vector Regression ◽  
Power Law ◽  
Conditional Expectation ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Asphaltene Precipitation ◽  
Committee Machine ◽  
Alternating Conditional Expectation

scholarly journals Prediction of asphaltene precipitation using support vector regression tuned with genetic algorithms

Petroleum ◽  
2016 ◽  
Vol 2 (3) ◽  
pp. 301-306 ◽  
Author(s):  
Mohammad Ghorbani ◽  
Ghasem Zargar ◽  
Hooshang Jazayeri-Rad
Keyword(s):  
Genetic Algorithms ◽  
Support Vector Regression ◽  
Support Vector ◽  
Asphaltene Precipitation

Estimation of asphaltene precipitation from titration data: a hybrid support vector regression with harmony search

2014 ◽  
Vol 26 (4) ◽  
pp. 789-798 ◽  
Author(s):  
Hadi Fattahi ◽  
Amin Gholami ◽  
Mohammad Sadegh Amiribakhtiar ◽  
Siyamak Moradi
Keyword(s):  
Support Vector Regression ◽  
Harmony Search ◽  
Support Vector ◽  
Asphaltene Precipitation ◽  
Titration Data

scholarly journals Insect Mass Estimation Based on Radar Cross Section Parameters and Support Vector Regression Algorithm

2020 ◽  
Vol 12 (11) ◽  
pp. 1903
Author(s):  
Cheng Hu ◽  
Shaoyang Kong ◽  
Rui Wang ◽  
Fan Zhang ◽  
Lianjun Wang
Keyword(s):  
Support Vector Regression ◽  
Cross Section ◽  
Radar Cross Section ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Feature Subset ◽  
Mass Estimation ◽  
Optimal Feature Subset ◽  
Optimal Feature

Radar cross section (RCS) parameters of insect targets contain information related to their morphological parameters, which are helpful for the identification of migratory insects. Several morphological parameter estimation methods have been presented. However, most of these estimations are performed based on polynomial fitting methods, using only one or two parameters, which may limit the estimation accuracy. In this paper, a new insect mass estimation method is proposed based on support vector regression (SVR). Several RCS parameters were extracted for the estimation of insect mass. Support vector regression based on recursive feature elimination (SVRRFE) was used to obtain the optimal feature subset. Specifically, a dataset including 367 specimens was included to evaluate the performance of the proposed method. Fifteen features were extracted and ranked. The optimal feature subset contained six features and the optimal mass estimation accuracy was 78%. Additionally, traditional insect mass estimation methods were analyzed for comparison. The results prove that the proposed method is more effective and accurate for insect mass estimation. It needs to be emphasized that the poor number of experimental insects available may limit the further improvement of estimation accuracy.

scholarly journals Support Vector Regression-Based Adaptive Divided Difference Filter for Nonlinear State Estimation Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hongjian Wang ◽  
Jinlong Xu ◽  
Aihua Zhang ◽  
Cun Li ◽  
Hongfei Yao
Keyword(s):  
State Estimation ◽  
Support Vector Regression ◽  
Traffic Control ◽  
Divided Difference ◽  
Tracking System ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Estimation Errors ◽  
Difference Filter ◽  
Divided Difference Filter

We present a support vector regression-based adaptive divided difference filter (SVRADDF) algorithm for improving the low state estimation accuracy of nonlinear systems, which are typically affected by large initial estimation errors and imprecise prior knowledge of process and measurement noises. The derivative-free SVRADDF algorithm is significantly simpler to compute than other methods and is implemented using only functional evaluations. The SVRADDF algorithm involves the use of the theoretical and actual covariance of the innovation sequence. Support vector regression (SVR) is employed to generate the adaptive factor to tune the noise covariance at each sampling instant when the measurement update step executes, which improves the algorithm’s robustness. The performance of the proposed algorithm is evaluated by estimating states for (i) an underwater nonmaneuvering target bearing-only tracking system and (ii) maneuvering target bearing-only tracking in an air-traffic control system. The simulation results show that the proposed SVRADDF algorithm exhibits better performance when compared with a traditional DDF algorithm.

scholarly journals Estimating Frequency by Interpolation Using Least Squares Support Vector Regression

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Changwei Ma
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
High Frequency ◽  
Fourier Coefficients ◽  
Frequency Estimation ◽  
Signal To Noise Ratio ◽  
Threshold Value ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Size Number

Discrete Fourier transform- (DFT-) based maximum likelihood (ML) algorithm is an important part of single sinusoid frequency estimation. As signal to noise ratio (SNR) increases and is above the threshold value, it will lie very close to Cramer-Rao lower bound (CRLB), which is dependent on the number of DFT points. However, its mean square error (MSE) performance is directly proportional to its calculation cost. As a modified version of support vector regression (SVR), least squares SVR (LS-SVR) can not only still keep excellent capabilities for generalizing and fitting but also exhibit lower computational complexity. In this paper, therefore, LS-SVR is employed to interpolate on Fourier coefficients of received signals and attain high frequency estimation accuracy. Our results show that the proposed algorithm can make a good compromise between calculation cost and MSE performance under the assumption that the sample size, number of DFT points, and resampling points are already known.

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