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Indexed by:会议论文

Date of Publication:2013-07-04

Included Journals:EI、Scopus

Volume:7951 LNCS

Issue:PART 1

Page Number:455-464

Abstract:The dynamic model of unmanned helicopter is a coupled nonlinear system. With respect to the identification problem for this model, extended least squares support vector machine (ELSSVM) is proposed. ELSSVM extends the solution space of structure parameters to improve the convergence performance. Base width of kernel function and regularization parameter of ELSSVM are minimized by differential evolution (DE). As compared to the traditional identification method for helicopter dynamic model, the proposed method omits the linear process and the trained model is closer to the helicopter dynamic model. The data-driven based experiments show that the proposed method takes a short training time and has a high identification accuracy. ? 2013 Springer-Verlag Berlin Heidelberg.