HR: 0800h
AN: H11F-0348    [Abstracts]
TI: SPARSE LEARNING MACHINES FOR CHAOTIC DYNAMIC SYSTEMS
AU: * Khalil, A F
EM: AKHALIL@CC.USU.EDU
AF: Abedalrazq Khalil, 1600 Canyon Roah, Logan, Ut 84322 United States
AB: Sparse learning machines provide a viable framework for modeling chaotic time-series data. A powerful state space reconstruction methodology using both Support Vector Machines (SVM) and relevance vector machines (RVM) within a multi-objective optimization framework is presented in this paper. The utility and practicality of the proposed approaches have been demonstrated on the time series of the Great Salk Lake (GSL) biweekly volume change from 1848 to 2004. A comparison of the two methods is made based on their predictive power and robustness. The reconstruction of the chaotic dynamics of the Great Salt Lake volume time series is attained using the most relevant feature subset of the training data. In this paper, efforts are also made to assess the uncertainty and robustness of the machines in learning and forecasting as a function of model structure, initial conditions, and bootstrapping samples. The resulting model will normally have a structure, including parameterization, that suits the information content of the available data, and can be used to develop time series forecasts for multiple lead times ranging from two weeks to several months.
DE: 1899 General or miscellaneous
DE: 1894 Instruments and techniques
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting