SVM Analysis Method for Infrared Spectra of Mixed Gas

2011 ◽  
Vol 58-60 ◽  
pp. 1681-1684
Author(s):  
Peng Bai ◽  
Yan Li ◽  
Peng Liu
Keyword(s):  
Infrared Spectra ◽  
Absolute Error ◽  
Experimental Result ◽  
Component Concentration ◽  
Support Vector ◽  
Model Parameters ◽  
Calibration Model ◽  
Mixed Gas ◽  
Spectra Analysis ◽  
Key Technologies

In order to solving the problem that the mass samples of mixed gas spectra data samples being unable to obtain, characteristic absorption spectrum line of the component gas for mixed gas being overlap, and the problem of randomness of component concentration distribution for mixed gas and so on, support vector machine is introduced for the infrared spectra analysis for the mixed gas. Key technologies as feature selection of spectra data samples, data preprocessing, SVM calibration model parameters optimization and level structure for spectrum analysis of a mixed gas is proposed in the paper. The influence of above-mentioned four key technologies to the analysis results is discussed by using experimental means. The experimental result shows that with adoption of the key technologies, the maximum absolute error of component concentration analysis for the mixed gas is 2.93%, and the maximum average absolute error is of 0.73%. The method can also be used for infrared spectra analysis for other mixed gas, and it has practical application value.

Research on Parameter Optimization of Mixed Gas IR SVM Calibration Model

2015 ◽  
Vol 1083 ◽  
pp. 97-103
Author(s):  
Jiang Wang ◽  
Peng Bai ◽  
Xiao Hu Duan ◽  
Yan Li
Keyword(s):  
Processing Method ◽  
Support Vector ◽  
Model Parameters ◽  
Calibration Model ◽  
Practical Application ◽  
Mixed Gas ◽  
Spectra Analysis ◽  
New Information ◽  
Penalty Factor ◽  
Measurement Results

A SVM calibration model combined with new information processing method of support vector machine and infrared spectroscopy is established. For the problem of model parameters affecting the analysis results, the optimization of the model parameters is studied through the experiment. The mixed gas containing hydrocarbon is used as an example, spectra data preprocessing, spectra analysis band, spectrometer scanning interval, types of kernel function for SVM calibration model, penalty factorC, and other parameters that affect the measurement results are optimized. The experimental results show that the accuracy of the analysis results can be improved in the case of the SVM calibration model optimized and the model has a practical application value.

scholarly journals Predicting the Istanbul Stock Exchange Index Return using Technical Indicators

2013 ◽  
Vol 2 (3) ◽  
pp. 111-117
Author(s):  
Senol Emir
Keyword(s):  
Stock Exchange ◽  
Absolute Error ◽  
Percentage Error ◽  
Support Vector ◽  
Model Parameters ◽  
Technical Indicators ◽  
Istanbul Stock Exchange ◽  
Vector Machines ◽  
Classical Linear Regression ◽  
Daily Returns

The aim of this study to examine the performance of Support Vector Regression (SVR) which is a novel regression method based on Support Vector Machines (SVM) approach in predicting the Istanbul Stock Exchange (ISE) National 100 Index daily returns. For bechmarking, results given by SVR were compared to those given by classical Linear Regression (LR). Dataset contains 6 technical indicators which were selected as model inputs for 2005-2011 period. Grid search and cross valiadation is used for finding optimal model parameters and evaluating the models. Comparisons were made based on Root Mean Square (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Theil Inequality Coefficient (TIC) and Mean Mixed Error (MME) metrics. Results indicate that SVR outperforms the LR for all metrics.

Coke Rate Prediction Model Based on Genetic Algorithm Optimized Support Vector Machine

2015 ◽  
Vol 740 ◽  
pp. 600-603
Author(s):  
You Jun Yue ◽  
Yan Fei Hu ◽  
Hui Zhao ◽  
Hong Jun Wang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Absolute Error ◽  
Support Vector ◽  
Model Parameters ◽  
Coke Rate ◽  
Rate Model ◽  
Iron And Steel ◽  
Model Based ◽  
Blast Furnace Coke

The accurate prediction model’s establishing of the blast furnace coke rate is important for optimizing the integrated production indicators of iron and steel enterprise. For the problem of accuracy of the model of coke rate, This paper established blast coke rate modeling with support vector machine algorithm, the model parameters of support vector machine was optimized by genetic algorithm, then a coke rate model based on support vector machine with the best parameters was built. Simulation results showed that: the forecasting model’s outcome, average absolute error and the mean relative error, was small which is based on genetic algorithm optimized SVM. coke rate model based on Genetic algorithm optimized support vector machine has high degree of accuracy and a certain practicality.

scholarly journals Stock Price Forecasting Framework based on the Support Vector Regression and Monte Carlo Method

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3716-3720
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Support Vector Regression ◽  
Stock Price ◽  
Mean Absolute Error ◽  
High Capacity ◽  
Absolute Error ◽  
Experimental Result ◽  
Support Vector ◽  
Price Forecasting

Stock market and its prices prediction are considered as one of the challenging task in financial forecasting. In my research, the framework is created based on the support vector regression (SVR) and Monte Carlo method to predict the stock price. The radial basis function (RBF) has high capacity, simpler design, and adopted for kernel function in SVR. The stock price of four companies Microsoft, Facebook, Amazon and Google is used to analyze the efficiency of the proposed method. The different parameters like mean square error (MSE), mean absolute error (MAE) measured to estimate the outcome of the proposed method. The experimental result showed the efficiency of the SVR-Monte Carlo in terms of error value. The MSE for the SVR-Monte Carlo in Google stock obtained as 0.2162 and MAE for the predicted value is 0.0164.

Rapid Determining Contents of the Rhubarb Anthraquinones Compounds by Support Vector Machines Modeling based on Near Infrared Spectra

2020 ◽  
Vol 16 ◽  
Author(s):  
Linqi Liu ◽  
JInhua Luo ◽  
Chenxi Zhao ◽  
Bingxue Zhang ◽  
Wei Fan ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Rapid Determination ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Near Infrared Spectra ◽  
Aloe Emodin ◽  
Relative Differences

BACKGROUND: Measuring medicinal compounds to evaluate their quality and efficacy has been recognized as a useful approach in treatment. Rhubarb anthraquinones compounds (mainly including aloe-emodin, rhein, emodin, chrysophanol and physcion) are its main effective components as purgating drug. In the current Chinese Pharmacopoeia, the total anthraquinones content is designated as its quantitative quality and control index while the content of each compound has not been specified. METHODS: On the basis of forty rhubarb samples, the correlation models between the near infrared spectra and UPLC analysis data were constructed using support vector machine (SVM) and partial least square (PLS) methods according to Kennard and Stone algorithm for dividing the calibration/prediction datasets. Good models mean they have high correlation coefficients (R2) and low root mean squared error of prediction (RMSEP) values. RESULTS: The models constructed by SVM have much better performance than those by PLS methods. The SVM models have high R2 of 0.8951, 0.9738, 0.9849, 0.9779, 0.9411 and 0.9862 that correspond to aloe-emodin, rhein, emodin, chrysophanol, physcion and total anthraquinones contents, respectively. The corresponding RMSEPs are 0.3592, 0.4182, 0.4508, 0.7121, 0.8365 and 1.7910, respectively. 75% of the predicted results have relative differences being lower than 10%. As for rhein and total anthraquinones, all of the predicted results have relative differences being lower than 10%. CONCLUSION: The nonlinear models constructed by SVM showed good performances with predicted values close to the experimental values. This can perform the rapid determination of the main medicinal ingredients in rhubarb medicinal materials.

Explanation Plus Prediction—The Logical Focus of Project Management Research

2021 ◽  
pp. 875697282199994
Author(s):  
Joseph F. Hair ◽  
Marko Sarstedt
Keyword(s):  
Project Management ◽  
Statistical Models ◽  
Predictive Power ◽  
Mean Absolute Error ◽  
Explanatory Power ◽  
Absolute Error ◽  
Model Parameters ◽  
Mean Square ◽  
Management Research ◽  
The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

scholarly journals Creation of Lunar and Hermean analogue mineral powder samples for solar wind irradiation experiments and mid-infrared spectra analysis

Icarus ◽  
2021 ◽  
Vol 365 ◽  
pp. 114492
Author(s):  
Noah Jäggi ◽  
André Galli ◽  
Peter Wurz ◽  
Herbert Biber ◽  
Paul Stefan Szabo ◽  
...  
Keyword(s):  
Solar Wind ◽  
Infrared Spectra ◽  
Spectra Analysis ◽  
Mid Infrared ◽  
Powder Samples ◽  
Mineral Powder

scholarly journals Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels

2021 ◽  
Vol 11 (8) ◽  
pp. 3705
Author(s):  
Jie Zeng ◽  
Panayiotis C. Roussis ◽  
Ahmed Salih Mohammed ◽  
Chrysanthos Maraveas ◽  
Seyed Alireza Fatemi ◽  
...  
Keyword(s):  
Radial Basis Function ◽  
Particle Velocity ◽  
Basis Function ◽  
Absolute Error ◽  
Peak Particle Velocity ◽  
Support Vector ◽  
Effective Parameters ◽  
Experimental Database ◽  
Interaction Detection ◽  
Radial Basis

This research examines the feasibility of hybridizing boosted Chi-Squared Automatic Interaction Detection (CHAID) with different kernels of support vector machine (SVM) techniques for the prediction of the peak particle velocity (PPV) induced by quarry blasting. To achieve this objective, a boosting-CHAID technique was applied to a big experimental database comprising six input variables. The technique identified four input parameters (distance from blast-face, stemming length, powder factor, and maximum charge per delay) as the most significant parameters affecting the prediction accuracy and utilized them to propose the SVM models with various kernels. The kernel types used in this study include radial basis function, polynomial, sigmoid, and linear. Several criteria, including mean absolute error (MAE), correlation coefficient (R), and gains, were calculated to evaluate the developed models’ accuracy and applicability. In addition, a simple ranking system was used to evaluate the models’ performance systematically. The performance of the R and MAE index of the radial basis function kernel of SVM in training and testing phases, respectively, confirm the high capability of this SVM kernel in predicting PPV values. This study successfully demonstrates that a combination of boosting-CHAID and SVM models can identify and predict with a high level of accuracy the most effective parameters affecting PPV values.

scholarly journals Predicting Corporate Financial Sustainability Using Novel Business Analytics

2018 ◽  
Vol 11 (1) ◽  
pp. 64 ◽  
Author(s):  
Kyoung-jae Kim ◽  
Kichun Lee ◽  
Hyunchul Ahn
Keyword(s):  
Financial Distress ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Support Vector ◽  
Model Parameters ◽  
Financial Sustainability ◽  
Business Analytics ◽  
Financial Distress Prediction ◽  
Proposed Model ◽  
Distress Prediction

Measuring and managing the financial sustainability of the borrowers is crucial to financial institutions for their risk management. As a result, building an effective corporate financial distress prediction model has been an important research topic for a long time. Recently, researchers are exerting themselves to improve the accuracy of financial distress prediction models by applying various business analytics approaches including statistical and artificial intelligence methods. Among them, support vector machines (SVMs) are becoming popular. SVMs require only small training samples and have little possibility of overfitting if model parameters are properly tuned. Nonetheless, SVMs generally show high prediction accuracy since it can deal with complex nonlinear patterns. Despite of these advantages, SVMs are often criticized because their architectural factors are determined by heuristics, such as the parameters of a kernel function and the subsets of appropriate features and instances. In this study, we propose globally optimized SVMs, denoted by GOSVM, a novel hybrid SVM model designed to optimize feature selection, instance selection, and kernel parameters altogether. This study introduces genetic algorithm (GA) in order to simultaneously optimize multiple heterogeneous design factors of SVMs. Our study applies the proposed model to the real-world case for predicting financial distress. Experiments show that the proposed model significantly improves the prediction accuracy of conventional SVMs.

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