China University of Mining and Technology, Xuzhou 221116, China.
Comput Intell Neurosci. 2018 Oct 29;2018:1365747. doi: 10.1155/2018/1365747. eCollection 2018.
Compressed sensing (CS) is an important research area of signal sampling and compression, and the essence of signal recovery in CS is an optimization problem of solving the underdetermined system of equations. Greedy pursuit algorithms are widely used to solve this problem. They have low computational complexity; however, their recovery performance is limited. In this paper, an intelligence recovery algorithm is proposed by combining the Bat Algorithm (BA) and the pruning technique in subspace pursuit. Experimental results illustrate that the proposed algorithm has better recovery performance than greedy pursuit algorithms. Moreover, applied to the microseismic monitoring system, the BA can recover the signal well.
压缩感知（CS）是信号采样和压缩的一个重要研究领域，CS 中的信号恢复本质上是求解欠定方程组的优化问题。贪婪追踪算法被广泛用于解决这个问题，其具有较低的计算复杂度，但恢复性能有限。本文提出了一种将蝙蝠算法（BA）和子空间追踪中的剪枝技术相结合的智能恢复算法。实验结果表明，所提出的算法具有比贪婪追踪算法更好的恢复性能。此外，将 BA 应用于微震监测系统中，能够很好地恢复信号。
