It introduces the SMC in backstepping design to modify the last step of backstepping algorithm and simplify the design of controller.
该方法通过在反演设计中引入滑模控制来改进反演的最后一步算法，并简化控制器的设计。
We integrate the Sparse Bayesian Learning algorithm into the SMC blind receiver to improve the performance under sparse channels.
我们将稀疏贝叶斯学习与序列蒙特卡罗盲均衡算法结合，提高了原算法的性能。
The experiment results demonstrate that the proposed algorithm enhances the precision of edge detection and satisfies the requirement of vision detecting in SMC ( surface mounting machine) mounting.
通过实验比较，认为该方法提高了边缘提取的精度，满足了贴片机视觉检测的要求。
Simulation results demonstrate that this algorithm can obtain good transient and steady state responses, and the algorithm is superior to the traditional PID control and SMC.
仿真结果表明，该算法可以获得良好的暂态和稳态响应，该方法优于传统的PID控制和滑模控制。
In addition, the new algorithm can save up to 50% execution time while possessing similar accuracy as SMC-PHDF.
同时，在两种算法跟踪精度接近的情况下，所提算法节省了50%的运行时间。
In addition, the new algorithm can save up to 50% execution time while possessing similar accuracy as SMC-PHDF.
同时，在两种算法跟踪精度接近的情况下，所提算法节省了50%的运行时间。
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