Support vector data description (SVDD) has become one of the most promising methods for one-class classification for finding the boundary of the training set. However, SVDD has a time complexity of O (N 3) and a space complexity of O (N 2). When dealing with very large sizes of training sets, e.g., a training set of the aeroengine gas path parameters with the size of N > 10 6 sampled from several months of flight data, SVDD fails. To solve this problem, a method called heuristic sample reduction (HSR) is proposed for obtaining a reduced training set that is manageable for SVDD. HSR maintains the classification accuracy of SVDD by building the reduced training set heuristically with the samples selected from the original. For demonstration, several artificial datasets and real-world datasets are used in the experiments. In addition, a practical example of the training set of the aeroengine gas path parameters is also used to compare the performance of SVDD based on the proposed HSR with conventional SVDD and other improved methods. The experimental results are very encouraging.

Few results are made on non-affine non-strict feedback nonlinear systems, which is a challenging problem in the control theory. In this paper, a novel control method based on an advanced backstepping and auto disturbance rejection is presented for a class of non-affine non-strict nonlinear feedback systems. The proposed advanced backstepping controller consists of differentiator and extended state observer, which are respectively used to approach the derivative of the virtual control and estimate the unknown part of the system. The framework of the proposed controller is both systematic and simple, and the assumptions have been relaxed. Moreover, the input to state stability analysis shows that the system states can asymptotically converge to an arbitrarily small region of equilibrium point. The simulation studies proved the effectiveness of the proposed design scheme.

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