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At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain computer interface (BCI), which made rapid progress including the improvement in computational accuracy, efficiency and robustness. However, these methods have deficiencies in real-time performance, generalization ability and the dependence of labeled sample in the analysis of the EEG signals. This mini review described the advantages and disadvantages of the SRC methods in the EEG signal analysis with the expectation that these methods can provide the better tools for analyzing EEG signals.

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Antilock braking system (ABS) has been designed to attain maximum negative acceleration and prevent the wheels from locking. Many efforts had been paid to design controller for ABS to improve the brake performance, especially when road condition changes. In this paper, an adaptive fuzzy fractional-order sliding mode controller (AFFOSMC) design method is proposed for ABS. The proposed AFFOSMC combines the fractional-order sliding mode controller (FOSMC) and fuzzy logic controller (FLC). In FOSMC, the sliding surface is PDα, which is based on fractional calculus (FC) and is more robust than conventional sliding mode controllers. The FLC is designed to compensate the effects of parameters varying of ABS. The tuning law of the controller is derived based on Lyapunov theory, and the stability of the system can be guaranteed. Simulation results demonstrate the effectiveness of AFFOSMC for ABS under different road conditions.

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