An enhanced extrapolation method based on particle swarm optimization-support vector regression to determine the...

This paper proposes a hybrid pretreating method in the support vector regression (SVR) machines to enhance the extrapolated performance of the traditional method based on particle swarm optimization (PSO)-SVR. This method is introduced into a novel domain wherein the friction coefficient (COF) is extrapolated between the aircraft tire and the runway surface. It includes two parts: the normalized data and the extrapolated method for COF. The first part develops a novel data normalized method, which allows the experiment data to follow sine distribution (SN) instead of traditional linear distribution. For the second part, the SVR with the training set initially extrapolates a COF, and the COF is subsequently added into the original training set to build a new training sample. This process is repeated until the extrapolation is finished. This method is named step-wise (SW) extrapolation. PSO is used to optimize the regularization constant C, the parameter gamma γ, and epsilon parameter ε in SVR. Finally, this study indicates that the proposed method (SN-SW-PSO-SVR) is more suitable for the extrapolation of the COF between the airplane tire and the runway surface than other extrapolated methods.