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The reliability of a product is not only important for customers to choose optimal products, but also necessary for manufacturers to design warranty strategies. While predicting the reliability of products accurately is always difficult. Several arithmetic was developed in the existed literature, such as Poisson models, Kalman filter etc. However, these methods hypotheses the distribution of the model,...
This paper proposes an accurate hybrid method based on support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to predict the tidal current speed and direction. In the proposed hybrid model, the ARIMA model captures the linear component of the tidal current, and the remained residual components are modeled by SVR. In order to capture the maximum linear components, the...
Degradation data is an important information source which is usually used to predict products' lifetime, for instance in accelerated degradation testing (ADT) and health management. Degradation data can be easier and cheaper obtained than failure data. As a result, it has been widely applied. However, due to some restrictions of funds and the development cycle, the degradation data of some products...
Aiming at increasing the precision of tunnel settlement prediction, a modified support vector machine (SVM) based on the dynamic on-line sliding window (Dolsw) technique is proposed. In the prediction model, the historically observational settlement data act as the learning samples. The nonlinear relationship between settlement data and influencing variables is established on the basis of on-line...
This paper uses the support vector machine (SVM) algorithm to study the prediction of corn production in Heilongjiang province, forms the sample set with the 1991-2008 data in Heilongjiang province, and set up the SVM model between factors and corn production. Use SVM on the input and output data for training and learning, approximate the implied function relationship by historical data, complete...
Support Vector Machines (SVM) represents a new and very promising approach to pattern recognition based on small dataset. The approach is systematic and properly motivated by Statistical Learning Theory (SLT). Training involves separating the classes with a surface that maximizes the margin between them. An interesting property of this approach is that it is an approximate implementation of Structural...
Bus travel time prediction is a vital part for both bus operation optimizing system and information service system. This paper reviews existing bus travel time prediction models and analyzes the strengths and weaknesses of each model. A bus travel time prediction model based on nu - Support Vector Regression is proposed, which uses the departure time of bus from origin stop that can reflect traffic...
During the last decade, support vector machines (SVM) have proved to be very successful tools for classification and regression problems. The representational performance of this type of networks is studied on a cavity flow facility developed to investigate the characteristics of aerodynamic flows at various Mach numbers. Several test conditions have been experimented to collect a set of data, which...
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