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.
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Mei, X., Feng, Y. (2013). Model Identification of an Unmanned Helicopter Using ELSSVM. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39065-4_55
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DOI: https://doi.org/10.1007/978-3-642-39065-4_55
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