Signal Sparse Decomposition Based on Adaptive Chaos Fruit Fly Optimization Algorithm
Ming Yang, Wei Liu, Beichen Chen
Available Online April 2015.
- https://doi.org/10.2991/icmra-15.2015.80How to use a DOI?
- Sparse decomposition; Adaptive Chaos Fruit Fly Optimization Algorithm(ACFOA); Global optimum; Computational complexity
- Sparse decomposition can represent signal with small number of atoms. But, its high computation complexity hinders practical application. Fruit Fly Optimization Algorithm (FOA) improves the efficiency of atoms’ searching, but the solution sometimes is not global optimum. In order to solve this problem, Adaptive Chaos Fruit Fly Optimization Algorithm (ACFOA) is presented. In this paper, signal sparse decomposition based on ACFOA is presented. The experiment shows that the reconstructed signal is satisfied, and the computational complexity is reduced greatly
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Ming Yang AU - Wei Liu AU - Beichen Chen PY - 2015/04 DA - 2015/04 TI - Signal Sparse Decomposition Based on Adaptive Chaos Fruit Fly Optimization Algorithm BT - Proceedings of the 3rd International Conference on Mechatronics, Robotics and Automation PB - Atlantis Press SP - 405 EP - 408 SN - 2352-538X UR - https://doi.org/10.2991/icmra-15.2015.80 DO - https://doi.org/10.2991/icmra-15.2015.80 ID - Yang2015/04 ER -