Direction of Arrival Based on the Multioutput Least Squares Support Vector Regression Model

The interferometer is a widely used direction-finding system with high precision. When there are comprehensive disturbances in the direction-finding system, some scholars have proposed corresponding correction algorithms, but most of them require hypothesis based on the geometric position of the array. The method of using machine learning that has attracted much attention recently is data driven, which can be independent of these assumptions. We propose a direction-finding method for the interferometer by using multioutput least squares support vector regression (MLSSVR) model. The application of this method includes the following: the construction of MLSSVR model training data, training and construction of the MLSSVR model, and the estimation of direction of arrival. Finally, the method is verified through numerical simulation. When there are comprehensive deviations in the system, the direction-finding accuracy can be effectively improved.

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By Using Normal Least Squares Support Vector Regression Model to Carry out Market Forecasting

Journal of Applied Sciences ◽

10.3923/jas.2013.4082.4086 ◽

2013 ◽

Vol 13 (19) ◽

pp. 4082-4086

Author(s):

Liu Hongxia

Keyword(s):

Regression Model ◽

Least Squares ◽

Support Vector Regression ◽

Support Vector ◽

Support Vector Regression Model

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Optimization for Raman Fiber Amplifiers Based on Least Squares Support Vector Regression Model

Acta Optica Sinica ◽

10.3788/aos201535.1123004 ◽

2015 ◽

Vol 35 (11) ◽

pp. 1123004

Author(s):

陈静 Chen Jing ◽

江灏 Jiang Hao ◽

刘暾东 Liu Tundong ◽

孙巧 Qiao Sun

Keyword(s):

Regression Model ◽

Least Squares ◽

Support Vector Regression ◽

Fiber Amplifiers ◽

Support Vector ◽

Support Vector Regression Model ◽

Raman Fiber Amplifiers

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Robust data-driven soft sensor based on iteratively weighted least squares support vector regression optimized by the cuckoo optimization algorithm

Journal of Natural Gas Science and Engineering ◽

10.1016/j.jngse.2014.11.017 ◽

2015 ◽

Vol 22 ◽

pp. 35-41 ◽

Cited By ~ 10

Author(s):

Masoud Behnasr ◽

Hooshang Jazayeri-Rad

Keyword(s):

Least Squares ◽

Support Vector Regression ◽

Optimization Algorithm ◽

Weighted Least Squares ◽

Soft Sensor ◽

Data Driven ◽

Support Vector ◽

Cuckoo Optimization Algorithm

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Forest Coverage Prediction Based on Least Squares Support Vector Regression Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.446-449.2978 ◽

2012 ◽

Vol 446-449 ◽

pp. 2978-2982

Author(s):

Fang Xiao

Keyword(s):

Regression Model ◽

Least Squares ◽

Support Vector Regression ◽

Prediction Error ◽

Support Vector ◽

Support Vector Regression Model ◽

Training Samples ◽

Forest Coverage ◽

Coverage Prediction ◽

Mean Prediction Error

Forest coverage prediction based on least squares support vector regression algorithm is presented in the paper.Forest coverage data of Heilongjiang from 1994 to 2005 are used to study the effectiveness of least squares support vector regression algorithm.The prediction results of the proposed least squares support vector regression model by using the training samples with the different dimensional input vector are given in the study. It can be seen that the prediction results of the proposed least squares support vector regression model by using the training samples with the 3-dimensional input vector have best prediction results.The comparison of forest coverage forecasting error between the proposed least squares support vector regression model and the support vector regression model is given, among which mean prediction error of the proposed least squares support vector regression model is 0.0149 and mean prediction error of the support vector regression model is 0.0322 respectively.The experimental results show that the proposed least squares support vector regression model has more excellent forest coverage forecasting results than the support vector regression model.

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Using Internet Search Trends and Historical Trading Data for Predicting Stock Markets by the Least Squares Support Vector Regression Model

Computational Intelligence and Neuroscience ◽

10.1155/2018/6305246 ◽

2018 ◽

Vol 2018 ◽

pp. 1-15 ◽

Cited By ~ 2

Author(s):

Ping-Feng Pai ◽

Ling-Chuang Hong ◽

Kuo-Ping Lin

Keyword(s):

Least Squares ◽

Support Vector Regression ◽

Stock Markets ◽

Google Trends ◽

Support Vector ◽

Internet Search ◽

Support Vector Regression Model ◽

Promising Alternative ◽

Market Values ◽

Hybrid Data

Historical trading data, which are inevitably associated with the framework of causality both financially and theoretically, were widely used to predict stock market values. With the popularity of social networking and Internet search tools, information collection ways have been diversified. Instead of only theoretical causality in forecasting, the importance of data relations has raised. Thus, the aim of this study was to investigate performances of forecasting stock markets by data from Google Trends, historical trading data (HTD), and hybrid data. The keywords employed for Google Trends are collected from three different ways including users’ definitions (GTU), trending searches of Google Trends (GTTS), and tweets (GTT) correspondingly. The hybrid data include Internet search trends from Google Trends and historical trading data. In addition, the correlation-based feature selection (CFS) technique is used to select independent variables, and one-step ahead policy is adopted by the least squares support vector regression (LSSVR) for predicting stock markets. Numerical experiments indicate that using hybrid data can provide more accurate forecasting results than using single historical trading data or data from Google Trends. Thus, using hybrid data of Internet search trends and historical trading data by LSSVR models is a promising alternative for forecasting stock markets.

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Predicting load on ground anchor using a metaheuristic optimized least squares support vector regression model: a Taiwan case study

Journal of Computational Design and Engineering ◽

10.1093/jcde/qwaa077 ◽

2020 ◽

Author(s):

Min-Yuan Cheng ◽

Minh-Tu Cao ◽

Po-Kun Tsai

Keyword(s):

Least Squares ◽

Support Vector Regression ◽

Influential Factors ◽

Percentage Error ◽

Support Vector ◽

Support Vector Regression Model ◽

Symbiotic Organisms Search ◽

Proper Actions ◽

Ground Anchors ◽

Ground Anchor

Abstract Failure of ground anchor is a major cause of landslides and severe natural hazards, especially in the highly developed mountainous areas such as New Taipei City. Accurately estimating load on ground anchors is thus essential for evaluating the stability status of slope to prevent landslide from happening. This study first employed correlation analyses to identify possible influential factors of load on ground anchors. Second, various artificial intelligence models were used to map the relationship of the found influencing factors with the current load on ground anchors. The results indicated that the symbiotic organisms search-optimized least squares support vector regression (SOS-LSSVR) model had the optimal accuracy by earning the smallest value of mean absolute percentage error (9.10%) and the most outstanding value of correlation coefficient (R = 0.988). The study applied the established inference model for the real case of estimating load on un-monitoring ground anchors. The analyzed results strongly advised administrators to conduct site surveying and patrolling more frequently to take early proper actions. In summary, the obtained results have demonstrated SOS-LSSVR as an effective alternative for the conventional subjective evaluation methods, which is able to rapidly provide accurate values of load on un-monitoring ground anchors.

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