The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The paper presents a method for prediction of multivariate chaotic time series, using radial basis function (RBF) neural network with the input phase space preprocessed by independent component analysis (ICA). Firstly, C-C method is used to respectively compute the embedding dimension and delay time for all variables, and we get a reconstructed initial multivariate input vector space which may be...
This paper compares the performance of Radial Basis Function and Support Vector Regression in time series forecasting. Both methods were trained to produce one step ahead forecasting on two chaotic time series data: Mackey Glass and Set A data from Santa Fe Competition. The criterions for comparison are based on the coefficient of determination (R2) and Root Mean Square Error (RMSE) between actual...
Based on the powerful nonlinear mapping ability of support vector machines, the predicting model of support vector machines in combination with takens' delay coordinate phase reconstruction of chaotic time series has been established. Yearly precipitation time series is of the chaotic characters, thus this model is used to try predicting the precipitation. Because of the peculiarity of precipitation...
The system identification/modeling problem looks for a suitably parameterized model, representing a given process. The parameters of the model are adjusted to optimize a performance function based on error between the given process output and identified process output. The linear system identification field is well established with many classical approaches whereas most of those methods cannot be...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.