Statistical learning theory

About: Statistical learning theory is a research topic. Over the lifetime, 1618 publications have been published within this topic receiving 158033 citations.

Village electrical load prediction by genetic algorithm and SVR

Abstract: Prediction of village electrical load is very important to manage village electrical load efficiently. Support vector regression (SVR) is a new learning algorithm based on statistical learning theory, which has a good time-series forecasting ability. As the choice of the best parameters of support vector regression is an important problem for support vector regression, and this problem will directly affect the regression accuracy of support vector regression model. Therefore, the GA-SVR predicting model is developed to predict village electrical load. The comparison results show that the new GA-SVR model can successfully gain the lowest prediction error values in electricity load forecasting.

Separating Well Log Data to Train Support Vector Machines for Lithology Prediction in a Heterogeneous Carbonate Reservoir

Abstract: The prediction of lithology is necessary in all areas of petroleum engineering. This means that to design a project in any branch of petroleum engineering, the lithology must be well known. Support vector machines (SVM’s) use an analytical approach to classification based on statistical learning theory, the principles of structural risk minimization, and empirical risk minimization. In this research, SVM classification method is used for lithology prediction from petrophysical well logs based on petrographic studies of core lithology in a heterogeneous carbonate reservoir in southwestern Iran. Data preparation including normalization and attribute selection was performed on the data. Well by well data separation technique was used for data partitioning so that the instances of each well were predicted against training the SVM with the other wells. The effect of different kernel functions on the SVM performance was deliberated. The results showed that the SVM performance in the lithology prediction of wells by applying well by well data partitioning technique is good, and that in two data separation cases, radial basis function (RBF) kernel gives a higher lithology misclassification rate compared with polynomial and normalized polynomial kernels. Moreover, the lithology misclassification rate associated with RBF kernel increases with an increasing training set size.

Forecasting Conditions of Reactor Coolant Pump Based on Support Vector Machine

Abstract: In the traditional fault diagnosis technology, classical life and reliability tests require sufficient sample size when diagnose the faults and forecast the future states. However, there is even less sample size for machinery products, especially for major equipment. The Support Vector Machine based on Statistical Learning Theory can solve this problem. In this paper, a forecast model for reactor coolant pump which combines LSSVM (Least Squares Support Vector Machine) and Time Series model is constructed. We studied the impact to forecast accuracy which caused by embedding dimension M, kernel function σ and regularization parameter γ. Meanwhile, the performance of LSSVM is verified by simulation data and field data. Then LSSVM is used to predict vibration signals of reactor coolant pump. As it is certified that the forecast data could match the actual data preferably and has achieved good results in forecasting field data.