Evolving Additive Tree Models for System Identification
نویسندگان
چکیده
To some extend, many complicated nonlinear maps are additive models of a number of linear and nonlinear terms. A single linear model or nonlinear model (i.e., a neural network model) has its limitation for approximating this class of maps. In this paper, a hybrid approach to evolve an additive tree model for a given problem is proposed. In this approach, tree-structure based evolution algorithm and a random search algorithm were employed to evolve the architecture and the parameters of the additive tree models, respectively. Simulation results for the prediction of chaotic time series, the reconstruction of polynomials and the identification of linear/nonlinear systems show the feasibility and effectiveness of the proposed method. Copyright c © 2003-2005 Yang’s Scientific Research Institute, LLC. All rights reserved.
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