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Predicting residual life of cable plays a key role in ship management. In this paper, the Grey Linear Regression Model is used for residual life prediction of shipboard cable under influence of multi-factors. The method is proved effectively by the comparison between the actual aging data and the data acquired by the traditional Arrhenius Equation.
In order to optimize the capacity of farmers' income persistently increasing, in this paper we analyze 8 factors affecting farmers' income increasing using PLS model based on the statistical data of Heilongjiang reclamation from 2000 to 2008. The study shows that wage incomes, agricultural subsidy and tax reducing still account for a great proportion of farmers' net incomes, but the level of agricultural...
Since reform and opening up to the outside world, rapid industrialization and urbanization in China have played an important role in the increase of energy consumption. China became the second energy consumption country all over the world in 2008. As a big country in energy consumption, forecasting energy consumption is one of the most important tools for energy policy setting. Although there are...
A performance degradation prediction method for multi-unit system with insufficient measurement data is proposed by integrating data recovering model, hidden Markov model and support vector regression (SVR) model. The development of the model includes three main parts. Part one, a principal component analysis (PCA) model is build based on normal state. Part two, a hidden Markov model(HMM) is trained...
In safety engineering, lower and upper explosion limits are the important indices to evaluate the safety of multi-component explosive gas mixture such as hydrogen and methane. There is a nonlinear dependence of explosion limits on the composition (components and theirs concentration) of multi-component explosive gas mixture. Therefore, a least square support vector regression (LS-SVR) model was proposed...
The forecast of grain production is not only an important resource for establishing agriculture policy but also is of great significance to ensure our nation's food security based on studying the change rule of our country grain production system. In view of the fact that the complexity and incomplete information of grain production system, the primary factors influencing the grain production is decided...
An approach based on chaos theory and fuzzy neural network (FNN) is proposed for chaotic time series prediction. Firstly, C-C algorithm is applied to estimate the delay time of chaotic signal. Grassberger-Procaccia (G-P) algorithm and least squares regression are employed to calculate the correlation dimension of chaotic signal simultaneously. Considering the difficulty in determining the number of...
In this paper, two modeling approaches (artificial neural network and regression model) are established and used to predict the fiber diameter of melt blowing nonwovens. By analyzing the results of the models, the effects of process parameters on fiber diameter can be predicted. The results demonstrated that the ANN model yields more accurate and stable predictions than regression model, which is...
This paper puts forward a method based on Logistic regression of emergency evacuation traffic trip generation forecasting model. Based on analysis of Xiamen residents earthquake evacuation survey data, the software of applied statistic SPSS could establish emergency evacuation traffic trip generation forecasting model by Logistic regression. This model could predict the probability of all evacuation...
Social networks have generated great expectations connected with their potential business value. The purpose of our research is to present that even a rudimentary application of data mining techniques can bring statistically significant improvement in marketing response accuracy throughout the virtual community. In our test the C&RT (classification and regression tree) approach was used to generate...
Prediction of regional logistics requirement provides a basis for the plan of regional logistics. In the study, support vector regression is presented to predict regional logistics requirement. The regional logistics data from 1996 to 2006 in Shanghai municipality are used as the application data of support vector regression. The comparison of prediction error between BP neural network and support...
The performance of a model, which is trained with offline data, is highly relied on the conditions in which the system is working. When the working conditions change, the prediction accuracy of the model will be reduced significantly. To solve this problem, we propose an adaptive SVR modeling method based on vector-field-smoothed (VFS) algorithm. This method can adapt the model quickly to new working...
In order to solve the problem of BP neural network, genetic support vector regression is presented to predict the lifetime of cylinder. Support vector regression (SVR) is a novel prediction algorithm based on structure risk minimization principles, which can lead to great generalization ability. In the genetic support vector regression model, the genetic algorithm is used to optimize the parameters...
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...
To accurately forecast port throughput is crucial to the success of any port operation policy. This paper attempts to create an optimal ensemble predictive model of port throughput by using regression models, grey model and artificial neural network. Years of historical data (Jan. 2001 to Dec. 2009) from major ports in China mainland are collected and the data of Dalian Port is used to establish and...
In this study we investigate the transferability of trained regression models to estimate solo run L2 cache stress of programs running on multi-core processors. We used machine learning to generate the trained regression models. Transferability of a regression model means how useful is a regression model (which is trained on one architecture) to predict the solo run L2 cache stress on another architecture...
Calculation of reserves in an oil reservoir and the determination of its performance and economics require good knowledge of its physical properties. Accurate determination of the pressure-volume-temperature (PVT) properties such as the bubble point pressure (Pb) and the oil formation volume factor (Bob) are important in the primary and subsequent development of an oil field. This paper proposes Support...
Statistical approach is often used in time series analysis. One of its uses is to predict the future trend of a time series. This can be applied in many applications such as solar radiation, economics and other researches related to time series. In this paper, we use several classic statistical models to fit the solar radiation time series. The goal is to find a suitable radiation model in predicting...
Solar energy is one of the most promising renewable energy sources. The generating capacity of this source however is highly dependent on the available sunlight, its duration and intensity. In order to integrate these types of sources into an existing power distribution system, system planners need an accurate model that predicts its generating capacity with the usage of easily accessible information...
Data mining deals with extracting or mining knowledge from large and infinite amount of stream data. It also handles the data quality with limited volume of disk or memory. In such traditional transaction environment it is impossible to perform frequent items mining because it requires analyzing which item is a frequent one to continuously incoming stream data and which is probable to become a frequent...
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