基于多目标粒子群算法的稀疏分解参数优化

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振动与冲击 ›› 2017, Vol. 36 ›› Issue (23) : 45-50.

论文

基于多目标粒子群算法的稀疏分解参数优化

王强张培林王怀光张云强李一宁

作者信息 +

Parametric optimization of sparse decomposition based on multi-objective particle swarm optimization algorithm

WANG Qiang, ZHANG Pei-lin, WANG Huai-guang, ZHANG Yun-qiang, LI Yi-ning

Author information +

文章历史 +

摘要

针对振动信号稀疏分解过程中存在的复杂参数设置问题，提出利用多目标粒子群算法进行稀疏分解参数优化，以实现振动信号的有效压缩。根据多目标粒子群理论，建立稀疏分解参数优化模型，确定粒子群优化目标函数、待优化参数，分析参数设置与目标函数之间的泛函关系。设计仿真实验，研究数据压缩指标之间的约束关系，指导多目标粒子群算法参数优化，改善数据压缩效果。应用实测数据，验证多目标粒子群算法的参数优化能力，实验结果表明：多目标粒子群算法能够优化振动信号稀疏分解参数，取得良好的振动信号数据压缩效果。

Abstract

Aiming at complex parameter setting in sparse decomposition process of vibration signals, the multi-objective particle swarm optimization algorithm was put forward for parameter optimization of sparse decomposition to realize effective compression of vibration signals. According to the multi-objective particle swarm theory, a model was established to determine objective function for the particle swarm optimization algorithm and parameters to be optimized, and the functional relation among parameters and the objective function was analyzed. A simulation test was designed to study constraint relations among indexes for data compression, guide the parameter optimization of the multi-objective particle swarm optimization algorithm, and improve the effects of data compression. The measured data were used to verify the parameter optimization ability of the multi-objective particle swarm optimization algorithm. The test results showed that the multi-objective particle swarm optimization algorithm can be used to optimize parameters for sparse decomposition of vibration signals, and get the good effects of data compression of vibration signals.

导出引用

王强张培林王怀光张云强李一宁. 基于多目标粒子群算法的稀疏分解参数优化[J]. 振动与冲击, 2017, 36(23): 45-50

WANG Qiang, ZHANG Pei-lin, WANG Huai-guang, ZHANG Yun-qiang, LI Yi-ning. Parametric optimization of sparse decomposition based on multi-objective particle swarm optimization algorithm[J]. Journal of Vibration and Shock, 2017, 36(23): 45-50

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